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AI Submissions for Thu Dec 18 2025

History LLMs: Models trained exclusively on pre-1913 texts

Submission URL | 651 points | by iamwil | 315 comments

History-locked LLMs: Researchers plan “Ranke-4B,” a family of time-capsule models

• What it is: An academic team (UZH, Cologne) is building Ranke-4B, 4B-parameter language models based on Qwen3, each trained solely on time-stamped historical text up to specific cutoff years. Initial cutoffs: 1913, 1929, 1933, 1939, 1946.
• Data and training: Trained from scratch on 80B tokens drawn from a curated 600B-token historical corpus; positioned as “the largest possible historical LLMs.”
• Why it’s different: The models are “fully time-locked” (no post-cutoff knowledge) and use “uncontaminated bootstrapping” to minimize alignment that would override period norms. The goal is to create “windows into the past” for humanities, social science, and CS research.
• Sample behavior: The 1913 model doesn’t “know” Adolf Hitler and exhibits period-typical moral judgments, including attitudes that would now be considered discriminatory. The authors include a clear disclaimer that they do not endorse the views expressed by the models.
• Openness: They say they’ll release artifacts across the pipeline—pre/posttraining data, checkpoints, and repositories.
• Status: Announced as an upcoming release; project hub is at DGoettlich/history-llms (GitHub).

History-locked LLMs: Researchers plan “Ranke-4B,” a family of time-capsule models What it is: Researchers from UZH and Cologne are developing "Ranke-4B," a series of language models trained exclusively on historical data up to specific cutoff years (starting with 1913). By using "uncontaminated bootstrapping," these 4B-parameter models aim to eliminate modern hindsight bias—for example, the 1913 model has no knowledge of WWI or Adolf Hitler and reflects the moral norms of its era. The project, intended for humanities and social science research, plans to release all checkpoints and datasets openly.

The Discussion: The concept of strictly "time-locked" AI sparked a debate blending literary analysis with geopolitical anxiety.

• Sci-Fi as Blueprint: Users immediately drew parallels to Dan Simmons’ Hyperion Cantos, specifically the plotline involving an AI reconstruction of the poet John Keats. This segued into a broader discussion on the "Torment Nexus" trope—the tendency of tech companies to build things specifically warned about in science fiction. Palantir was cited as a prime example, with users noting the irony of a surveillance company naming itself after a villain’s tool from Lord of the Rings.
• Simulating Leadership: The conversation pivoted to a related report about the CIA using chatbots to simulate world leaders for analysts. While some users dismissed this as "laughably bad" bureaucratic theater or a "fancy badge on a book report," others speculated that with enough sensory data and private intelligence, modeling distinct psychological profiles (like Trump vs. Kim Jong Un) might actually be feasible.
• Prediction vs. Hindsight: Commenters debated the utility of these models. Some viewed them as generating "historical fiction" rather than genuine insights, while others argued that removing "hindsight contamination" is the only way to truly understand how historical events unfolded without the inevitability bias present in modern LLMs.

How China built its ‘Manhattan Project’ to rival the West in AI chips

Submission URL | 416 points | by artninja1988 | 505 comments

China’s EUV breakthrough? Reuters reports that a government-run “Manhattan Project”-style effort in Shenzhen has produced a prototype extreme ultraviolet (EUV) lithography machine—technology the West has long monopolized via ASML. The system, completed in early 2025 and now under test, reportedly spans nearly an entire factory floor and was built by a team of former ASML engineers who reverse‑engineered the tool. Huawei is said to be involved at every step of the supply chain.

Why it matters

• EUV is the chokepoint behind cutting-edge chips for AI, smartphones, and advanced weapons. Breaking ASML’s monopoly would undercut years of U.S.-led export controls.
• If validated and scalable, China could accelerate domestic production of sub‑7nm chips, loosening reliance on Western tools.

Reality check

• Reuters cites two sources; independent verification isn’t public.
• Building a prototype is far from high-volume manufacturing. Throughput, uptime, defectivity, overlay, and ecosystem pieces (masks, pellicles, resists, metrology) are massive hurdles.
• Legal and geopolitical fallout (IP investigations, tighter sanctions, pressure on the Netherlands/ASML) is likely.

What to watch next

• Independent specs: numerical aperture, source power, throughput, overlay.
• Test wafer yields and any tape-outs at advanced nodes.
• How quickly domestic suppliers fill critical EUV subcomponents.
• Policy responses from the U.S., EU, and the Netherlands—and any actions targeting ex‑ASML talent.

If confirmed, this would be the most significant challenge yet to the export-control regime built around EUV.

Here is a summary of the discussion:

Material Conditions vs. Cultural Narratives The discussion opened with a debate on whether checking reported breakthroughs against "national character" is useful. User ynhngyhy noted that EUV machines "weren't made by God," implying that reverse engineering is simply a matter of time and resources, though they cautioned that corruption and fraudulent projects have historically plagued China's semiconductor sector. Others, like snpcstr and MrSkelter, argued that cultural explanations for technological dominance are "fairy tales"; they posit that U.S. dominance has been a result of material conditions (being the largest rich country for a century) and that China’s huge population and middle class will inevitably shift those statistics.

Comparative Inefficiencies A significant portion of the thread pivoted to comparing structural weaknesses in both nations. While users acknowledged corruption as a drag on China, dngs and others highlighted systemic inefficiencies in the U.S., citing exorbitant healthcare costs, poor urban planning (car dependency), and the inability to build infrastructure (subways) at reasonable prices compared to China’s high-speed rail network. The consensus among these commenters was that while the U.S. benefits from efficiency in some sectors, it wastes immense resources on litigation and protectionism.

The "Brain Drain" Model vs. Domestic Scale The role of talent acquisition fueled a debate on diversity and immigration. Users discussed the U.S. model of relying on global "brain drain" to import top talent, contrasting it with China's strategy of generating massive domestic engineering capacity.

• mxglt noted a generational divide in massive Chinese tech firms: older leaders often view the West as the standard, while a younger wave of "techno-optimists" and nationalists believe they can overtake incumbents.
• A sub-thread explored U.S. visa policy, with users like cbm-vc-20 suggesting the U.S. should mandate or incentivize foreign graduates to stay to prevent them from taking their skills back to compete against the U.S.

Skepticism and Pragmatism Overall, the sentiment leaned away from dismissing the report based on ideology. As heavyset_go summarized, relying on cultural arguments to predict economic velocity is like "Schrodinger's cat"—often used to explain why a country can't succeed until they suddenly do.

Firefox will have an option to disable all AI features

Submission URL | 514 points | by twapi | 484 comments

I’m ready to summarize, but I don’t have the submission. Please share one of the following:

• The Hacker News thread URL
• The article link or pasted text
• The title plus key points or notable comments

If you want a full daily digest, tell me:

• How many top stories to include and for which date/time window
• Any preference on length (e.g., 3–5 sentence summaries vs. deeper dives)

By default, I’ll deliver:

• What happened and why it matters
• Key technical/market takeaways
• Notable community reactions (top comments/themes)
• Links for further reading and a quick TL;DR

Here is a summary of the provided discussion regarding Mozilla, AI, and browser development.

The Story: Mozilla’s AI Focus vs. Core Browser Health

What happened: A discussion erupted regarding Mozilla’s recent push into AI features. The community sentiment is largely critical, arguing that the backlash against AI isn't simply "anti-AI," but rather frustration that Mozilla is chasing "fads" (crypto, VR, AI) while neglecting the core browser and stripping away power-user features.

Why it matters: Firefox remains the only significant alternative to the Chromium browser engine monopoly (Chrome, Edge, Brave, etc.). As Mozilla struggles for financial independence from Google, their strategy to bundle revenue-generating services (like AI or VPNs) is clashing with their core user base, who prioritize privacy, performance, and deep extensibility.

Key Technical & Market Takeaways

• The "Fad" Cycle vs. Sustainability: Commenters argue Mozilla has a history of "jumping fads" (allocating resources to VR or Crypto) instead of maintaining the browser core. However, counter-arguments suggest this is a survival tactic: "Mozilla isn't jumping fads, it's jumping towards money." Because users rarely pay for browsers directly, Mozilla chases where the investment capital flows (currently AI).
• Extensibility vs. Security: A major friction point remains the death of XUL and NPAPI (old, powerful extension systems) in favor of WebExtensions and Manifest v2/v3.
  • The Critique: Users feel the browser has become a "bundled garbage" suite rather than an extensible platform.
  • The Technical Reality: While deep access (XUL) allowed for total customization, it was a security nightmare and hampered performance. The debate continues on whether modern WebAPIs (WebUSB, WebNFC) are sufficient replacements or if they just turn the browser into a bloated operating system.
• The "Platform" Debate: There is disagreement on the intent of a browser. Some view the web as a "de-facto standard application platform" that requires hardware access (USB/Serial), while others see this scope creep as a security risk that turns the browser into a resource-heavy OS layer.

Notable Community Reactions

• The "Power User" Lament: User tlltctl initiated the discussion by arguing that the real issue isn't AI itself, but the lack of "genuine extensibility." They argue Mozilla should remove bundled features and instead provide APIs so users can add what they want (including AI) via extensions.
• The "Fork" Fantasy: gncrlstr and others voiced a desire for a "serious fork" of Firefox that removes the "nonsense" and focuses purely on the browser engine, though others acknowledged the immense cost and difficulty of maintaining a modern browser engine.
• The Irony of "Focus": User forephought4 proposed a sarcastic/idealistic "5-step plan" for Mozilla to succeed (building a Gmail competitor, an office suite, etc.). Another user, jsnltt, pointed out the irony: the plan calls for "focusing on the core," yet simultaneously suggests building a massive suite of non-browser products.
• Implementation Ideas: mrwsl suggested a technical middle ground: rather than bundling a specific AI, Mozilla should architect a "plug-able" system (similar to Linux kernel modules or Dtrace) allowing users to install their own AI subsystems if they choose.

TL;DR

Users are angry that Mozilla is bundling AI features into Firefox, viewing it as another desperate attempt to monetize a "fad" rather than fixing the core browser. The community wants a fast, stripped-down, highly extensible browser, but acknowledges the harsh reality that "core browsers" don't attract the investor funding Mozilla needs to survive against Google.

Note: The input text was heavily abbreviated (vowels removed). This summary reconstructs the likely intent of the conversation based on standard technical context and the visible keywords.

T5Gemma 2: The next generation of encoder-decoder models

Submission URL | 141 points | by milomg | 26 comments

Google’s next-gen encoder‑decoder line, T5Gemma 2, brings major architectural changes and Gemma 3-era capabilities into small, deployable packages—now with vision, long context, and broad multilingual support.

What’s new

• Architectural efficiency:
  • Tied encoder/decoder embeddings to cut parameters.
  • “Merged” decoder attention that fuses self- and cross-attention in one layer, simplifying the stack and improving parallelization.
• Multimodality: Adds a lightweight vision encoder for image+text tasks (VQA, multimodal reasoning).
• Long context: Up to 128K tokens via alternating local/global attention, with a separate encoder improving long-context handling.
• Multilingual: Trained for 140+ languages.

Model sizes (pretrained, excluding vision encoder)

• 270M-270M (~370M total)
• 1B-1B (~1.7B)
• 4B-4B (~7B) Designed for rapid experimentation and on-device use.

Performance highlights

• Multimodal: Outperforms Gemma 3 on several benchmarks despite starting from text-only Gemma 3 bases (270M, 1B).
• Long context: Substantial gains over both Gemma 3 and the original T5Gemma.
• General capabilities: Better coding, reasoning, and multilingual performance than corresponding Gemma 3 sizes.
• Post-training note: No instruction-tuned checkpoints released; reported post-training results use minimal SFT (no RL) and are illustrative.

Why it matters

• Signals a renewed push for encoder‑decoder architectures—especially compelling for multimodal and very long-context workloads—while keeping parameter counts low enough for edge/on-device scenarios.

Availability

• Pretrained checkpoints now on arXiv (paper), Kaggle, Hugging Face, Colab, and Vertex AI (inference).

T5Gemma 2: Architecture and Use Cases Discussion focused on the practical distinctions between the T5 (Encoder-Decoder) architecture and the dominant Decoder-only models (like GPT).

• Architecture & Efficiency: Users clarified confusion regarding the model sizes (e.g., 1B+1B). Commenters noted that due to tied embeddings between the encoder and decoder, the total parameter count is significantly lower than simply doubling a standard model, maintaining a compact memory footprint.
• Fine-Tuning Constraints: There was significant interest in fine-tuning these models. Experienced users warned that fine-tuning a multimodal model solely on text data usually results in "catastrophic forgetting" of the vision capabilities; preserving multimodal performance requires including image data in the fine-tuning set.
• Use Case Suitability: Participants discussed why one would choose T5 over Gemma. The consensus was that Encoder-Decoder architectures remain superior for specific "input-to-output" tasks like translation and summarization, as they separate the problem of understanding the input (Encoding) from generating the response (Decoding).
• Google Context: A member of the T5/Gemma team chimed in to point users toward the original 2017 Transformer paper to understand the lineage of the architecture.

FunctionGemma 270M Model

Submission URL | 211 points | by mariobm | 54 comments

FunctionGemma: a tiny, on-device function-calling specialist built on Gemma 3 (270M)

What’s new

• Google released FunctionGemma, a 270M-parameter variant of Gemma 3 fine-tuned for function calling, plus a training recipe to specialize it for your own APIs.
• Designed to run locally (phones, NVIDIA Jetson Nano), it can both call tools (structured JSON) and talk to users (natural language), acting as an offline agent or a gateway that routes harder tasks to bigger models (e.g., Gemma 3 27B).

Why it matters

• Moves from “chat” to “action” at the edge: low-latency, private, battery-conscious automation for mobile and embedded devices.
• Emphasizes specialization over prompting: on a “Mobile Actions” eval, fine-tuning boosted accuracy from 58% to 85%, highlighting that reliable tool use on-device benefits from task-specific training.
• Built for structured output: Gemma’s 256k vocab helps tokenize JSON and multilingual inputs efficiently, reducing sequence length and latency.

When to use it

• You have a defined API surface (smart home, media, navigation, OS controls).
• You can fine-tune for deterministic behavior rather than rely on zero-shot prompting.
• You want local-first agents that handle common tasks offline and escalate complex ones to a larger model.

Ecosystem and tooling

• Train: Hugging Face Transformers, Unsloth, Keras, NVIDIA NeMo.
• Deploy: LiteRT-LM, vLLM, MLX, Llama.cpp, Ollama, Vertex AI, LM Studio.
• Available on Hugging Face and Kaggle; demos in the Google AI Edge Gallery app; includes a cookbook, Colab, and a Mobile Actions dataset.

Demos

• Mobile Actions (offline assistant: calendar, contacts, flashlight).
• TinyGarden (voice → game API calls like plantCrop/waterCrop).
• Physics Playground (browser-based puzzles with Transformers.js).

Caveats

• The strongest results come after fine-tuning on your specific tools and schemas.
• At 270M, expect limits on complex reasoning; treat it as a fast, reliable tool-caller and router, not a general-purpose heavy thinker.

Here is a summary of the discussion:

A Google Research Lead participated in the thread canyon289 (OP) engaged extensively with commenters, positioning FunctionGemma not as a general-purpose thinker, but as a specialized component in a larger system. He described the model as a "starter pack" for training your own functions, designed to be the "fast layer" that handles simple tasks locally while escalating complex reasoning to larger models (like Gemma 27B or Gemini).

The "Local Router" Architecture There was significant interest in using FunctionGemma as a low-latency, privacy-preserving "switchboard."

• The Workflow: Users proposed a "dumb/fast" local layer to handle basic system interactions (e.g., OS controls) and route deeper reasoning prompts to the cloud. OP validated this, noting that small, customizable models are meant to fill the gap between raw code and frontier models.
• Security: When asked about scoping permissions, OP advised against relying on the model/tokens for security. Permissions should be enforced by the surrounding system architecture, not the LLM.

Fine-Tuning Strategy Users asked how to tune the model without "obliterating" its general abilities.

• Data Volume: The amount of data required depends on input complexity. A simple boolean toggle (Flashlight On/Off) needs very few examples. However, a tool capable of parsing variable inputs (e.g., natural language dates, multilingual queries) requires significantly more training data to bridge the gap between user intent and structured JSON.
• Generality: To maintain general reasoning while fine-tuning, OP suggested using a low learning rate or LoRA (Low-Rank Adaptation).

Limitations and Concerns

• Context Window: Replying to a user wanting to build a search-based Q&A bot, OP warned that the 270M model's 32k context window is likely too small for heavy RAG (Retrieval-Augmented Generation) tasks; larger models (4B+) are better suited for summarizing search results.
• Reasoning: The model is not designed for complex zero-shot reasoning or chaining actions without specific fine-tuning. One user questioned if the cited 85% accuracy on mobile actions is "production grade" for system tools; others suggested techniques like Chain-of-Thought or quorum selection could push reliability near 100%.
• No Native Audio: Several users asked about speech capabilities. OP clarified that FunctionGemma is text-in/text-out; it requires a separate ASR (Automatic Speech Recognition) model (like Whisper) to handle voice inputs.

Demos & Future Users were impressed by browser-based WebML demos (games controlled by voice/actions). OP hinted at future releases, suggesting 2026 would be a significant year for bringing more modalities (like open-weights speech models) to the edge.

Local WYSIWYG Markdown, mockup, data model editor powered by Claude Code

Submission URL | 27 points | by wek | 5 comments

Nimbalyst is a free, local WYSIWYG markdown editor and session manager built specifically for Claude Code. It lets you iterate with AI across your full context—docs, mockups, diagrams, data models (via MCP), and code—without bouncing between an IDE, terminal, and note-taking tools. Sessions are first-class: tie them to documents, run agents in parallel, resume work later, and even treat past sessions as context for coding and reviews. Everything lives locally with git integration, so you can annotate, edit, embed outputs, and build data models from your code/doc set in one UI. It’s available for macOS, Windows, and Linux; free to use but requires a Claude Pro or Max subscription.

Nimbalyst: Local WYSIWYG Editor for Claude Code The creator, wk, introduced Nimbalyst as a beta tool designed to bridge the gap between Claude Code and local work contexts, allowing users to manage docs, diagrams, and mockups in a unified interface. Key features highlighted included iterating on HTML mockups, integrating Mermaid diagrams, and tying sessions directly to documents. Early adopter iman453 responded positively, noting they had already switched their default terminal to the tool. Additionally, the creator confirmed to radial_symmetry that the implementation focuses on a WYSIWYG markdown editing experience rather than a plain text view.

AI helps ship faster but it produces 1.7× more bugs

Submission URL | 202 points | by birdculture | 164 comments

CodeRabbit’s new analysis compares AI-generated pull requests to human-written ones and finds AI contributions trigger significantly more review issues—both in volume and severity. The authors note study limitations but say the patterns are consistent across categories.

Key findings

• Overall: AI PRs had ~1.7× more issues.
• Severity: More critical and major issues vs. human PRs.
• Correctness: Logic/correctness issues up 75% in AI PRs.
• Readability: >3× increase with AI contributions.
• Robustness: Error/exception handling gaps nearly 2× higher.
• Security: Up to 2.74× more security issues.
• Performance: Regressions were rarer overall but skewed toward AI.
• Concurrency/deps: ~2× more correctness issues.
• Hygiene: Formatting problems 2.66× higher; naming inconsistencies nearly 2×.

Why this happens (per the authors)

• LLMs optimize for plausible code, not necessarily correct or project-aligned code.
• Missing repository/domain context and implicit conventions.
• Weak defaults around error paths, security, performance, concurrency.
• Drift from team style/readability norms.

What teams can do

• Provide rich context to the model (repo, architecture, constraints).
• Enforce style and conventions with policy-as-code.
• Add correctness rails: stricter tests, property/fuzz testing, typed APIs.
• Strengthen security defaults: SAST, secrets scanning, dependency policies.
• Steer toward efficient patterns with prompts and linters/perf budgets.
• Use AI-aware PR checklists.
• Get help reviewing and testing AI code (automated and human).

Bottom line: AI can speed up coding, but without strong guardrails it increases defects—especially in correctness, security, and readability. Treat AI code like a junior contributor: give it context, enforce standards, and verify rigorously.

Based on the discussion, commenters largely validated the report’s findings, drawing heavily on an analogy to "VB (Visual Basic) Coding" to describe the specific type of low-quality code AI tends to produce.

The "VB Coding" and "Zombie State" Problem The most prominent theme was the comparison of AI code to bad "Visual Basic" habits, specifically the use of On Error Resume Next or blind null-checking.

• Swallowing Exceptions: Users argued that AI optimizes for "not crashing" rather than correctness. It tends to insert frequent, unthoughtful null checks or try/catch blocks that suppress errors silently.
• The Consequence: While the application keeps running, it enters a corrupted or "zombie" state where data is invalid, making root-cause debugging nearly impossible compared to a hard crash with a stack trace.
• Defensive Clutter: One user noted AI operates on a "corporate safe style," generating defensive code intended to stop juniors from breaking things, but resulting in massive amounts of cruft.

Automated Mediocrity Commenters discussed the quality gap between senior developers and AI output.

• Average Inputs: Since models are trained on the "aggregate" of available code, they produce "middle-of-the-road" or mediocre code.
• The Skill Split: "Subpar" developers view AI as a godsend because it works better than they do, while experienced developers find it irritating because they have to fight the AI to stop it from using bad patterns (like "stringly typed" logic or missing invariants).
• The Long-Term Risk: Users worried about the normalization of mediocrity, comparing LLMs to "bad compilers written by mediocre developers."

The Productivity Illusion vs. Tech Debt Several users shared anecdotes suggesting that the speed gained in coding is lost in maintenance.

• The "StackOverflow" Multiplier: Users compared AI to the "copy-paste developer" of the past who blindly stole code from StackOverflow, noting that AI just automates and accelerates this bad behavior.
• Real-world Costs: One user described a team where 40% of capacity is now spent on tech debt and rework caused by AI code. They cited an example where an AI-generated caching solution looked correct but silently failed to actually cache anything.
• Design Blindness: Commenters emphasized that AI is good at syntax ("getting things on screen") but fails at "problem solving" and proper system design.

Valid Use Cases Despite the criticism, some users offered nuance on where AI still succeeds:

• Explainer Tool: One user noted that while they don't trust AI to write code, it is excellent at reading and explaining unfamiliar open-source packages or codebases, effectively replacing documentation searches.
• Boilerplate: For simple CRUD/business apps or "tab-complete" suggestions, it remains useful if the developer strictly enforces architectural rules.

AI Submissions for Wed Dec 17 2025

Gemini 3 Flash: Frontier intelligence built for speed

Submission URL | 1072 points | by meetpateltech | 564 comments

Google launches Gemini 3 Flash: “frontier intelligence” tuned for speed and price

• What’s new: Gemini 3 Flash is the fastest, most cost‑efficient model in the Gemini 3 family, meant to deliver Pro‑grade reasoning with “Flash‑level” latency. It’s now the default model in the Gemini app and AI Mode in Search, and available to developers via the Gemini API (Google AI Studio, Gemini CLI), the new agentic dev platform Google Antigravity, Android Studio, and for enterprises via Vertex AI and Gemini Enterprise.

• Performance claims:

  • Reasoning/knowledge: GPQA Diamond 90.4%; Humanity’s Last Exam 33.7% (no tools); MMMU Pro 81.2% (comparable to Gemini 3 Pro).
  • Coding agents: SWE-bench Verified 78%, beating Gemini 3 Pro and the 2.5 series, per Google.
  • “LMArena Elo” cited for overall performance; vendor says it rivals larger frontier models.

• Speed and cost:

  • 3x faster than Gemini 2.5 Pro at a “fraction of the cost” (based on “Artificial Analysis” benchmarking, not an industry standard).
  • Dynamic thinking: modulates compute, using ~30% fewer tokens on average than 2.5 Pro on typical tasks.
  • Pricing: $0.50 per 1M input tokens ($0.0005/1K), $3 per 1M output tokens ($0.003/1K); audio input $1 per 1M tokens.

• Use cases highlighted: agentic workflows, real‑time interactive apps, coding assistants, multimodal/video analysis, data extraction, visual Q&A, in‑game assistance, and rapid design‑to‑code with A/B testing.

• Scale note: Google says since Gemini 3’s launch it has processed over 1T tokens/day on its API.

Why it matters: If the claims hold up outside Google’s benchmarks, Flash looks aimed squarely at low‑latency, high‑frequency workloads—coding agents, real‑time UI helpers, and mobile—by pushing the price/quality/speed Pareto frontier. As always, treat vendor benchmarks and the “Artificial Analysis” speed claim with caution until third‑party tests land.

Speed and efficiency verified: Early adopters report the claims regarding speed and cost hold up in production, with one user noting the model rivals GPT-4 and Claude 3.5 Sonnet in reasoning while offering significantly lower latency. Users discussing internal benchmarks for multimodal/video pipelines observed that Flash processes tasks nearly twice as fast as the Pro variant and, in specific edge cases, actually outperformed the larger model.

The "Skeptic" Test: A self-identified generative AI skeptic admitted that Gemini 3 Flash was the first model to correctly answer a specific, niche "trick question" from their personal benchmark suite—a test that previous models (including Gemini 2.5 Flash and others) had consistently failed. This sparked a debate on how to evaluate LLMs: while skeptics use niche trivia to test for hallucinations, developers argued that the true value lies in data transformation, classification, and reasoning tasks (such as analyzing SQL query execution plans) rather than using the model as a database of obscure facts.

Benchmarking strategies: The thread evolved into a technical exchange on how to properly test these models, with users sharing strategies for "prompt-to-JSON" dashboards and workflows that combine subtitle data with screen recordings to verify accuracy, latency, and token variance across different model versions.

AWS CEO says replacing junior devs with AI is 'one of the dumbest ideas'

Submission URL | 1014 points | by birdculture | 510 comments

AWS CEO: Replacing junior devs with AI is “one of the dumbest ideas”

• On WIRED’s The Big Interview, AWS CEO Matt Garman argues companies shouldn’t cut junior engineers to “save” with AI. His three reasons:

  • Juniors are often the most fluent with AI tools, squeezing more output from copilots and agents (the post cites surveys showing higher daily AI use among early-career devs).
  • They’re the least expensive headcount, so eliminating them rarely moves the cost needle—and layoffs can backfire (the post claims many firms later rehire at higher cost).
  • Cutting entry-level roles breaks the talent pipeline, starving teams of future leaders and fresh ideas; the post cites industry growth forecasts to underscore long-term demand.

• Garman’s broader view: AI will reshape jobs and boost productivity, expanding what companies build rather than shrinking teams. He stresses keeping CS fundamentals and mentoring in-house so orgs don’t hollow out their engineering ladders.

• Why it matters: The near-term temptation to replace juniors with AI collides with long-term capability building. If juniors are today’s best AI “power users,” sidelining them may reduce, not increase, ROI from AI adoption.

• Note: Statistics (Stack Overflow usage, layoff cost outcomes, workforce growth) are cited by the post; treat them as claims from the source interview/summary.

Based on the discussion, the community focused heavily on the cultural and systemic value of junior engineers beyond their code output.

The "Dumb Question" Heuristic The most prominent thread argues that juniors provide a crucial service by asking "dumb questions."

• Exposing Nonsense: Users argued that juniors, unburdened by "company memory" or complex abstractions, often startle seniors into realizing that existing systems or explanations make no sense.
• Losing Face: There was significant debate regarding who is "allowed" to ask simple questions. While some argued juniors can do so safely, others contended that seniors and executives possess the political capital to ask "dumb" questions, whereas juniors might be penalized for incompetence during performance reviews.
• Imposter Syndrome: Several commenters noted that seniors are often more terrified of asking basic questions than juniors due to the pressure to appear expert, leading to silent acceptance of bad architecture.

Workplace Culture and Safety The feasibility of keeping juniors (and asking questions) was tied directly to organizational toxicity:

• Weaponized Incompetence: Users from competitive markets (e.g., specific mentions of UK/Australia) noted that in some environments, asking questions is "weaponized" to wound an employee's reputation.
• Short-termism: Commenters suggested that companies looking to replace juniors with AI are likely "bad places to work," run by managers prioritizing short-term stock bumps over the long-term health of the "company memory."

Juniors and AI Hallucinations A tangible concern raised regarding AI-native juniors is the "big game" phenomenon. Experienced leads observed that some modern juniors, relying heavily on LLMs (like Claude), mimic the confidence and "hallucinations" of the models—producing articulate but technically hollow explanations that hide knowledge gaps more effectively than previous generations.

AI's real superpower: consuming, not creating

Submission URL | 238 points | by firefoxd | 173 comments

AI’s real superpower: reading everything you’ve already written, not writing something new. The author wired their Obsidian vault (years of notes, meeting reflections, book highlights) to an AI and stopped asking it to “create.” Instead, they ask it to surface patterns and forgotten insights across time.

Concrete wins:

• From 50 recent 1:1s, AI found performance issues tend to precede tooling complaints by 2–3 weeks.
• It traced a personal shift in thinking about tech debt (from “stuff to fix” to “signals about system evolution”) around March 2023.
• It connected design choices between Buffer’s API and the author’s own app, highlighting repeated patterns worth reusing—or rethinking.

Thesis: The bottleneck isn’t writing; humans create fine with the right inputs. The bottleneck is consumption—reading, remembering, and connecting everything. AI changes retrieval by enabling concept queries, pattern detection across years, and cross-context linking.

How to try it:

• Centralize your notes (e.g., Obsidian).
• Index with embeddings and give AI/RAG access.
• Ask questions about patterns, evolutions, and connections—not for drafts.
• Document relentlessly for your future self.

What to watch: privacy of personal corpora, hallucinations, quality of notes, and cost. The payoff: faster problem-solving, better decisions, and compounding insight from your own experience.

While the submission focuses on the personal productivity benefits of AI "reading" for you, the Hacker News discussion immediately pivots to the darker implications of this capability when applied by governments and corporations: Mass Surveillance and The Panopticon.

The "Reading" Bottleneck was a Feature, not a Bug Commenters argue that the human inability to consume vast amounts of information (the bottleneck the author solves) was actually a natural barrier against totalitarianism.

• The Panopticon: Several users note that the physical infrastructure for total surveillance (cameras everywhere) already exists, but the ability to process that data was limited by human labor. AI solves this, allowing automated analysis of millions of camera feeds or years of browsing history instantly.
• Psychological Profiling: Users fear AI will be used to build sophisticated profiles to predict behavior, identify "dissidents," or manipulate consumers.
• The "Stupid/Powerful" Risk: One user counters the idea that these models need to be perfect to be dangerous. They argue the real risk is "stupid people in powerful positions" believing in correlation-based pseudoscientific AI (likened to phrenology) to make decisions on hiring, border control, or policing.

Central Planning and Data Integrity A sub-thread draws parallels between AI governance and the fall of the Soviet Union.

• Information Processing: Users debate whether modern LLMs/IoT could solve the information processing issues that doomed the Soviet planned economy ("Klaus Schwab's Fourth Industrial Revolution").
• Garbage In/Garbage Out: Skeptics argue that AI doesn't solve the human incentive to lie. Just as Soviet factory managers faked production numbers, modern inputs will still be gamed, meaning AI would just process bad data more efficiently.

Defensive Strategies: Local vs. Cloud Echoing the article’s technical setup but for different reasons, users advocate for Local AI:

• Privacy as Survival: One user, identifying as an immigrant, specifically fears using ChatGPT for research because those logs could theoretically be cross-referenced by border control.
• The Conclusion: The consensus moves toward "disconnected private computing" (running local LLMs) not just for better notes, but to avoid feeding the centralized profiling machine.

The State of AI Coding Report 2025

Submission URL | 127 points | by dakshgupta | 106 comments

The State of AI Coding (2025): What’s changing on the ground

Key productivity shifts

• PRs are bigger and denser: median PR size +33% (57 → 76 lines); lines changed per file +20% (18 → 22).
• Output per dev up 76% (4,450 → 7,839 LOC); medium teams (6–15 devs) up 89% (7,005 → 13,227 LOC).
• Takeaway: AI assistance is increasing throughput and packing more change per PR—good for velocity, harder for review.

Tooling and ecosystem

• AI memory: mem0 dominates at 59% of downloads.
• Vector DBs: still fragmented; Weaviate leads at 25% with five others in the 10–25% band.
• AI rules files: CLAUDE.md leads with 67% adoption; 17% of repos use all three formats.
• SDK momentum: Anthropic SDK hits 43M monthly downloads (8× since April); Pydantic AI 3.7× to 6M.
• LLMOps: LiteLLM 4× to 41M monthly; LangSmith bundled via LangChain continues to ride along.

Model providers: gap is closing

• OpenAI SDK still largest at 130M monthly downloads.
• Anthropic grew 1,547× since Apr 2023; OpenAI:Anthropic ratio shrank from 47:1 (Jan ’24) to 4.2:1 (Nov ’25).
• Google trails at 13.6M.

Benchmarks: latency, throughput, cost (coding-agent backends)

• TTFT (p50): Anthropic is snappiest for first token—Sonnet 4.5 ~2.0s, Opus 4.5 ~2.2s; GPT-5 series ~5–5.5s; Gemini 3 Pro ~13.1s.
• Throughput (p50): GPT-5-Codex ~62 tok/s and GPT-5.1 ~62 tok/s lead; Anthropic mid-tier (18–19 tok/s); Gemini 3 Pro ~4 tok/s.
• Cost (8k in / 1k out, normalized to GPT-5 Codex = 1×): GPT-5.1 = 1×, Gemini 3 Pro = 1.4×, Sonnet 4.5 = 2×, Opus 4.5 = 3.3×.
• Net: Anthropic feels faster to start; OpenAI finishes long generations faster and cheaper; Gemini lags on both.

Methodology notes

• Identical prompts and parameters across models (temperature 0.2, top_p 1.0, max_tokens 1024), exponential backoff, warmups before TTFT, p25/p50/p75 reported.

Research shaping 2025 systems

• DeepSeek-V3 (671B MoE, 37B active per token): Multi-Head Latent Attention shrinks KV caches; sparse routing keeps GPUs busy; multi-token prediction densifies learning signals—efficiency over raw size.
• Qwen2.5-Omni: separates perception (audio/vision encoders) from language model for real-time text–audio–video reasoning; introduces time-aligned multimodal RoPE.

Why it matters

• Teams are shipping more per dev with denser PRs, AI memory is consolidating, vector DBs remain a horse race, and OpenAI’s lead is narrowing fast.
• For coding agents: pick Anthropic for responsiveness, OpenAI for high-throughput/long outputs/cost, and plan infra around multi-provider routing as the stack matures.

Based on the discussion, here is a summary of the comments:

The Validity of Lines of Code (LOC) as a Metric The primary point of contention in the thread is the report’s use of LoC to measure increased productivity. The majority of commenters strongly criticized this metric, arguing that code should be viewed as a liability (cost) rather than an asset.

• Liability vs. Asset: User conartist6 and others argued that celebrating more lines of code is akin to "business money cranking" or fraud, noting that senior engineers often reduce complexity by deleting lines. a_imho suggested we should count "lines spent" rather than produced.
• Goodhart’s Law: rdr-mttrs offered a "warehouse analogy": if you measure productivity by how many times items are moved, workers will move things needlessly. Similarly, measuring LoC incentivizes bloat rather than solved problems.
• Counterpoint: Rperry2174 suggested that while LoC is a bad quality metric, it remains a reasonable proxy for practice and output, provided the code is functioning and merged.

Quality, Churn, and Maintainability Skepticism ran high regarding whether valid code equates to good software.

• Bugs and Reverts: nm and refactor_master questioned if the 76% increase in speed comes with a 100% increase in bugs, asking for data on "reverted" code or churn rates.
• Technical Debt: zkmn and wrs highlighted that machines can easily generate volume (like assembly code), but the true cost lies in long-term maintainability and readability for humans.
• Platform Influence: 8note suggested LLMs might effectively be spamming ticket queues and codebases, creating an illusion of velocity while increasing administrative overhead.

Author Interaction and Data Insights dkshgpt (co-founder of Greptile, the submission author) engaged with the feedback:

• Defense of Methodology: The author acknowledged that LoC is imperfect but noted they struggled to find a reliable automated quality metric, finding "LLM-as-a-judge" to be inaccurate.
• Specific Trends: Responding to ChrisbyMe, the author noted a "Devin" sub-trend: full-sync coding agents are writing the highest proportion of code at the largest companies (F500), while "ticket-to-PR" workflows fail at startups.
• Data Sources: Confirmed that provider/tooling download charts were based on public data (npm/PyPi), while coding stats came from internal analysis of billions of lines of code.

Anecdotal Evidence

• mgclp validated the report's graphs against their own experience, noting that while LLMs increase "logic/agent" productivity, they lack discernment. They also observed that dev productivity collapses when LLMs go offline due to connectivity issues, indicating a heavy reliance on the tools.

A16z-backed Doublespeed hacked, revealing what its AI-generated accounts promote

Submission URL | 277 points | by grahamlee | 160 comments

Hack exposes a16z-backed phone farm flooding TikTok with AI influencers

404 Media reports that a hacker took control of Doublespeed, an Andreessen Horowitz–backed startup running a 1,100-device phone farm to operate at least hundreds of AI-generated TikTok accounts pushing products—often without clearly labeling them as ads. The hacker says he disclosed the vulnerability on Oct 31 and still had backend access at time of publication; Doublespeed didn’t respond to requests for comment.

Why it matters:

• Industrialized astroturfing: Using real phones helps evade platform anti-bot checks, suggesting a larger, harder-to-detect market for covert influencer ads.
• Ad transparency risk: Undisclosed promotions could violate TikTok rules and FTC endorsement guidelines.
• Security and governance: A VC-backed growth outfit allegedly left a door open long after disclosure, raising questions about diligence and liability.
• Platform enforcement: If confirmed, it pressures TikTok to detect phone farms and AI persona networks more effectively.

Key details:

• Scale: ~1,100 smartphones under central control; hundreds of AI-run accounts.
• Control: Hacker claims ongoing access to the farm and backend.
• Content: The operation promoted various products, often without ad disclosures, per the report.
• Company response: No comment from Doublespeed at publication time.

Here is today’s digest of the top story on Hacker News.

Hack exposes a16z-backed phone farm flooding TikTok with AI influencers A hacker has revealed that Doublespeed, a startup backed by Andreessen Horowitz, is operating a physical "phone farm" of approximately 1,100 devices to manage hundreds of AI-generated TikTok accounts. The investigation by 404 Media details how the operation used these accounts to push products without proper ad disclosures. The hacker, who claimed to still have backend access at the time of publication, stated they disclosed the vulnerability in late October. The story highlights the growing scale of "industrialized astroturfing," where real hardware is used to evade anti-bot detection, raising significant questions about platform integrity and the due diligence of top-tier venture capital firms.

Summary of the Discussion: The discussion on Hacker News focused heavily on the realization of the "Dead Internet Theory" and the ethics of venture capital.

• The Dead Internet Reality: Many users expressed resignation, noting that this story confirms their suspicion that social media is increasingly composed of "bots talking to bots." Commenters argued that platforms like Reddit and TikTok are being paralyzed by "professional propaganda" and disinformation, making constructive human discourse difficult.
• VC Ethics Scrutiny: A significant portion of the thread expressed shock and disgust that a top-tier firm like a16z would find such an operation. One commenter noted the irony that bot farms were historically associated with adversarial state actors (like Russia or China), but are now being normalized by Silicon Valley capital as legitimate "growth."
• The CEO’s Persona: Users dug into the Twitter/X feed of Doublespeed’s CEO, describing it as "sickening" and indicative of a mindset that views the "enshittification" of common digital spaces as a goal rather than a consequence.
• Detection and The Future: There was debate over how effective current anti-bot measures are. While some argued that niche communities (physically moderated forums) are the last refuge, others feel that "default" social media experiences are already obsolete dumpsters of fake content. One user referenced Dune’s "Butlerian Jihad," suggesting a coming societal rejection of machines that mimic the human mind.

AI Isn't Just Spying on You. It's Tricking You into Spending More

Submission URL | 100 points | by c420 | 63 comments

AI isn’t just watching you—it’s nudging your wallet. A New Republic piece surveys how companies are using AI-backed data harvesting and dynamic pricing to quietly extract more money from consumers.

Key points:

• Loyalty programs as surveillance loopholes: Vanderbilt researchers say “opt-in” programs let firms track far beyond purchases. Example: McDonald’s digital Monopoly requires app redemption; its privacy policy allows precise location, browsing, app and social data to train AI that infers psychological traits and targets engagement. With a 250M-user goal, the report says McDonald’s could hold profiles at near “national intelligence” scale.
• Personalized price shifts: An investigation by Groundwork Collaborative, Consumer Reports, and More Perfect Union found Instacart prices varied for the same items across users—about 75% of items fluctuated, sometimes by up to 23%, potentially costing heavy users up to $1,200/year. AI enables granular, user-specific pricing based on location/IP, often without clear disclosure.
• Policy lag: Rep. Greg Casar has proposed limits on AI-driven pricing and wage setting; prospects are dim federally. The article notes a Trump EO threatening funds to states with “cumbersome” AI rules, while some states plan to regulate anyway. Polls show 61% of Americans want more control over AI use.

Why it matters: Opaque, AI-driven price discrimination makes budgeting harder and can exploit captive “loyalty” users. Expect growing scrutiny of dark patterns, disclosure requirements, and state-level regulation.

Predictive Accuracy and the Target Myth A significant portion of the discussion revisits the famous anecdote about Target predicting a teen’s pregnancy before her father knew. Users debate the story's veracity, with some suggesting it is often exaggerated; rather than "galaxy-brain" AI, the system likely used simple association rules (buying zinc and unscented lotion triggers baby coupons) or lucky timing. However, commenters shared personal corroborations of invasive health targeting, such as a user whose wife received aggressive marketing for baby formula and diapers shortly after starting fertility treatments—raising suspicions that medical benefit providers or partners (like Carrot Fertility) might be selling data, or that online research patterns are being aggressively monetized.

The "Dumb" vs. "Omniscient" Algorithm While the article portrays AI as a sophisticated psychological profiler, several commenters argue that current ad targeting is often clumsy or "dumb." Examples included receiving ads in languages the user doesn't speak or for random products based solely on IP association (e.g., getting ads for a friend’s music tastes after visiting their house). Users noted that seeing ads for specific conditions (like GLP-1 weight loss drugs) might simply be broad demographic targeting or "carpet bombing" rather than a sign that an AI has diagnosed the user.

Systemic Critique: AI vs. Capitalism A philosophical subthread argues that the core issue is not AI itself, but capitalism using AI to remove inefficiencies in wealth extraction. Users expressed concern that instead of a "Star Trek" post-scarcity future, AI is being used to perfect price discrimination and consumption debt. The debate touched on whether personalized advertising provides any genuine utility (product discovery) or if the fundamental conflict of interest—where the advertiser’s profit motive outweighs the consumer’s benefit—requires "draconian" regulation to fix.

AI capability isn't humanness

Submission URL | 50 points | by mdahardy | 53 comments

AI capability isn’t the same as humanness, argue the authors, and scaling models will widen—not close—that gap. While LLMs can produce human-like outputs, they run on fundamentally different constraints: unlike humans’ bounded, metabolically limited, serial reasoning with tiny working memory and high time pressure, LLMs can scale parameters and training data almost arbitrarily, attend to whole contexts in parallel, and take generous seconds to respond. Humans learn from sparse, attention-filtered, lived experience; LLMs learn from vast, uniform corpora and store “memory” diffusely in weights, relying on pattern matching rather than stepwise recall. The piece claims these architectural and resource differences drive distinct problem-solving strategies, so similarity in outputs is largely superficial. Implication: alignment and interpretability should pivot from outcome-based “human-likeness” to process-focused evaluation—measuring how models think, not just what they say.

Based on the discussion, commenters debated the fundamental differences between human and AI learning, primarily focusing on data efficiency, sensory input, and the role of evolution.

Key themes included:

• Data Volume and Modality: Users contrasted the "unbounded" text training of LLMs (trillions of tokens) against human learning (millions of words). However, ForceBru and others argued this comparison is flawed because humans process continuous, high-bandwidth sensory streams (vision, touch, physics) that dwarf text-only data.
• The Necessity of Sensory Experience: There was significant debate over whether physical interaction is required for intelligence. mdhrdy cited a study where a model trained on video from a baby’s head-mounted camera learned word-object mappings without physical manipulation. emp17344 argued that sensory data isn't a prerequisite for general intelligence, citing Helen Keller and blind people as proof that high cognition exists without full sensory fidelity, though dprk pushed back, arguing that a brain totally divorced from input cannot be intelligent.
• Evolution as Pre-training: crtsft and layer8 noted that humans benefit from millions of years of evolutionary "pre-training" encoded in a compact genome (approx. 750MB). This suggests human intelligence relies on efficient, evolved algorithms/priors, whereas LLMs rely on brute-force statistical correlations.
• The "Duck Test" for Intelligence: Finally, users debated if the internal mechanism matters if the output is good. gmslr argued that language capability does not equal reasoning or agency. In contrast, ACCount37 contended that if the model "walks and quacks like a duck," it is effectively doing abstract thinking, proposing that high-dimensional matrix math is simply what thought looks like at the mechanical level.

OpenAI Is Maneuvering for a Government Bailout

Submission URL | 23 points | by boh | 8 comments

OpenAI Is Maneuvering for a Government Bailout (The American Prospect) The Prospect’s Ryan Cooper argues that OpenAI’s business model only works with public backstops, citing eye-popping reported losses (billions in 2024 and 2025) and CFO Sarah Friar’s recent suggestion at a WSJ tech conference that government loan guarantees might be needed to fund AI’s massive compute buildout. Friar later clarified she was advocating structural support for AI broadly, not OpenAI specifically, and also floated “financial innovation” like sweetheart deals with chipmakers and revenue-sharing from third-party ChatGPT use. Cooper frames this as pre-bailout positioning—socializing risk to sustain a sky-high valuation—and doubts AI’s near-term productivity payoff, arguing that the most proven money-makers so far are harmful uses. He’s skeptical that Bain’s projected $2T in AI revenues by 2030 is realistic without subsidies and dismisses VC dreams of fully automating labor. Big picture: a sharp, critical take on the economics and public policy of scaling frontier AI—and whether taxpayers will be asked to underwrite it.

OpenAI Is Maneuvering for a Government Bailout

Commenters were generally skeptical of the article's premise that OpenAI qualifies for a traditional bailout, arguing that the company lacks the systemic risk profile of a major bank. One user noted that if OpenAI collapses, the industry won't crash; users and developers will simply migrate to Google or open-source models, making any government funding appear more like "grift" than a necessary rescue.

Other points of discussion included:

• Negotiation Tactics: Some users theorized this is a strategic play by OpenAI—anchoring high by floating massive government backing so that "light-touch regulation" appears to be a reasonable compromise.
• Political Feasibility: There were doubts regarding the political will in Washington to underwrite a tech company's losses, with users suggesting Congress has zero appetite for such a move.
• Inefficiency of Subsidies: Skeptics predicted that if the government did provide a "backstop" for AI infrastructure, it would likely result in years of wasteful, failed pilot programs rather than a sustainable economic outcome.

Windows 11 will ask consent before sharing personal files with AI after outrage

Submission URL | 79 points | by jinxmeta | 50 comments

Microsoft says Windows 11’s upcoming AI “agents” won’t be able to read your files unless you grant permission. In updated docs (Dec 5), the company clarifies that agents are optional, run in a separate “agentic workspace,” and must explicitly request access to your “known folders” (Desktop, Documents, Downloads, Music, Pictures, Videos).

Key points:

• Consent first: When an agent (e.g., Copilot, Researcher, Analyst) needs files, Windows will prompt you to Allow always, Ask every time, or Never allow (currently “Not now,” with “Never” coming).
• Coarse-grained control: Permissions are per-agent but all-or-nothing across the six known folders; you can’t grant access to some folders and not others.
• Manageable in Settings: Each agent gets its own page to control file access and “Connectors” (OneDrive, Google Drive) plus Agent Connectors via Model Context Protocol (letting agents interact with apps like File Explorer and System Settings).
• Availability: In preview builds 26100.7344+ (24H2) and 26200.7344+ (25H2).

Why it matters: After criticism that agent-based features could overreach or misbehave, Microsoft is adding a clearer consent model—though the lack of per-folder granularity may frustrate privacy-conscious users.

The discussion surrounding Microsoft's latest clarification on AI agents reflects deep skepticism regarding the company's respect for user consent and privacy:

• Dark Patterns and Consent: Users extensively criticized the UI choices, specifically the use of "Not now" instead of a ubiquitous "Never" button, and the tendency for Windows to nag users repeatedly until they concede. Commenters described these tactics as "wizard" interfaces that frame data harvesting as "security" or "protection" to trick non-technical users.
• The OS as a Storefront: A prevailing sentiment is that Windows 11 has shifted from a productivity tool to a "digital storefront" designed to push recurring subscriptions (OneDrive, Microsoft 365) and harvest telemetry, treating the user as the product rather than the customer.
• Linux Migration: As is common with Windows privacy news, the thread spurred a debate about switching to Linux. While some jokingly referenced the eternal "Year of the Linux Desktop," others noted that the "friction" of Windows (bloat, ads, privacy invasions) is finally driving gamers and power users to viable alternatives like KDE Plasma and Pop!_OS, though the lack of retail Linux laptops remains a barrier for general consumers.
• Negotiation Tactics: Several commenters theorized that Microsoft intentionally announces egregious privacy invasions only to "soften" them later; this anchors the user's expectations, making the slightly-less-invasive version seem like a victory for user feedback, even though it still oversteps boundaries.

California judge rules that Tesla engaged in deceptive marketing for Autopilot

Submission URL | 69 points | by elsewhen | 15 comments

California DMV judge: Tesla’s Autopilot/FSD marketing was deceptive; 60-day fix window before potential sales suspension

• What happened: A California administrative law judge found Tesla’s marketing of Autopilot and Full Self-Driving (FSD) deceptive, saying it suggests fully autonomous capability when the systems require an attentive human driver.
• Penalty structure: The judge proposed a 30-day suspension of Tesla’s licenses to sell and manufacture in California. The DMV adopted the ruling with changes:
  • Tesla gets 60 days to correct deceptive or confusing claims.
  • If not corrected, the DMV will suspend Tesla’s sales license in California for 30 days.
  • The DMV is staying the manufacturing-license suspension, so factory operations continue uninterrupted.
• Why deceptive: The order says a “reasonable consumer” could believe “Full Self-Driving Capability” means safe operation without constant driver attention, which is wrong legally and technologically.
• Tesla’s stance: In a statement via FGS Global, Tesla called it a consumer-protection order over the term “Autopilot,” noting no customers were cited as complaining; it says California sales continue uninterrupted.
• Context:
  • The DMV first filed false advertising accusations in 2022.
  • Tesla has since renamed the option “Full Self-Driving (Supervised).”
  • A class action in federal court (N.D. Cal.) separately alleges Tesla misled buyers about self-driving capabilities.
  • TSLA shares closed at a record Tuesday amid investor enthusiasm for robotaxis/driverless tech.

What’s next: Tesla has 60 days to adjust marketing; failure could trigger a 30-day sales suspension in California. Manufacturing isn’t currently at risk under the DMV’s stay. This could ripple into how ADAS features are named and marketed industry-wide.

Here is a summary of the discussion on Hacker News:

Regulatory Delays and Responsibility A major focal point of the discussion was the timing of this ruling. Users debated why regulators waited years to deem the terminology unacceptable, with some arguing that this long period of tolerance created a "regulatory vacuum" that implicitly allowed the ambiguity to persist. Others pushed back on this logic, drawing a parallel to the SEC’s failure to catch Bernie Madoff earlier—arguing that regulatory slowness does not validate deceptive behavior. There was speculation that regulators may have held back due to optimistic expectations that the technology would catch up to the marketing, or simply due to bureaucratic inertia.

Deceptive Terminology vs. Reality Commenters were generally critical of Tesla’s naming conventions. While one user noted that "Autopilot" is a valid aviation term (where pilots still monitor systems), they conceded that the general public misunderstands it. The "Full Self-Driving" moniker was widely viewed as indefensible given that the system still requires active supervision. Users pointed out that despite the addition of "Supervised" to the name, customers are still paying thousands of dollars for features that depend on future regulatory approvals that may never arrive.

Penalties and Liability Several users expressed frustration with the penalty structure, suggesting that a stayed suspension isn't enough. Some called for refunds for customers who bought the software under "false pretenses." When a commenter identifying as a libertarian asked why no one is being jailed if laws were broken, others clarified the legal distinction: this was a ruling by an administrative law judge regarding civil regulations, not a criminal court case involving fraud charges.

AI Submissions for Tue Dec 16 2025

AI will make formal verification go mainstream

Submission URL | 714 points | by evankhoury | 368 comments

• Why it’s been niche: Formal verification demands rare expertise and massive effort. Classic example: the seL4 microkernel had ~8.7k lines of C, but its proof took ~200k lines of Isabelle and ~20 person‑years—about 23 lines of proof and half a person‑day per implementation line.
• What’s changed: LLMs are getting good at writing proof scripts (Rocq/Coq, Isabelle, Lean, F*, Agda). Even if they hallucinate, the proof checker rejects invalid steps and forces retries. That shifts the economics: machine time replaces PhD time.
• Why it matters for AI code: If code is AI‑generated, you’d rather have a machine‑checked proof than human review. Cheap, automated proofs could make verified code preferable to “artisanal” hand‑written code with latent bugs.
• New bottleneck: Specs, not proofs. The hard part becomes expressing the properties you actually care about. AI can help translate between natural language and formal specs, but subtle requirements can get lost—so human judgment still matters.
• The vision: Developers declare specs; AI synthesizes both implementation and proof. You don’t read the generated code—just trust the small, verified checker—much like trusting a compiler’s output today. The remaining hurdle is cultural, not technical.

The "Specification Gap" remains the blocker. Commenters argued that the primary obstacle to formal verification isn't the difficulty of writing proofs, but the industry's inability to strictly define what software is actually supposed to do. Users noted that "industry refuses to decide" on requirements; consequently, AI might simply help verify a program against a flawed or incomplete specification, resulting in "perfectly verified bugs."

Skepticism regarding "Day-to-Day" utility. Several users felt formal verification addresses problems typical developers don't face. While valid for kernels or compression libraries, it doesn't solve common issues like confusing UIs, changing third-party APIs, or messy data integration. For many, formal verification adds significant friction to refactoring and iteration, which is where most development time is spent.

Strong type systems are the current "mainstream." A significant portion of the discussion debated whether strong type systems (like in Rust or Haskell) already serve as "lite" formal verification.

• Pro-Types: Proponents argued that types enforce invariants and eliminate entire classes of bugs (like null pointer exceptions), effectively acting as documentation and allowing fearless refactoring.
• Anti-Friction: Critics argued that strict typing creates a high barrier to entry and adds unnecessary overhead for simple tasks (like GUI scripts or string shuffling), where the "ceremony" of the code outweighs the safety benefits.

The "Expert in the Loop" problem. Users warned that if an AI agent gets stuck while generating a verified implementation, the developer is left in a worse position: needing to debug machine-generated formal logic without the necessary expertise. Some predicted the future is more likely to be AI-augmented property-based testing and linters rather than full mathematical proofs.

alpr.watch

Submission URL | 833 points | by theamk | 387 comments

alpr.watch: Track local surveillance debates and ALPR deployments

• What it is: A live map that surfaces city/county meeting agenda items about surveillance tech—especially automated license plate readers (ALPRs), Flock cameras, and facial recognition—so residents can show up, comment, or organize.
• How it works: It scans public agendas for keywords like “flock,” “ALPR,” and “license plate reader,” pinning current and past meetings. You can toggle past meetings, view known ALPR camera locations (via deflock.me reports), and subscribe to email alerts by ZIP code and radius.
• Why it matters: The site argues municipalities are rapidly adopting surveillance (it cites 80,000+ cameras) that enable mass tracking and cross-agency data sharing, often with limited public oversight.
• Extras: An explainer on ALPRs and Flock Safety, a “slippery slope” primer on scope creep, and links to advocacy groups (EFF, ACLU, Fight for the Future, STOP, Institute for Justice).
• Caveats: Agenda parsing can miss items or generate false positives; coverage depends on accessible agendas. The site notes data before mid-December may be unverified, while future flags are moderator-approved.

Link: https://alpr.watch

Art and Awareness Projects A significant portion of the discussion focused on creative ways to visualize pervasive surveillance. Users brainstormed "sousveillance" art projects, such as painting QR codes or stencils on the ground within the blind spots of public cameras; when passersby scan the code, they would be linked to the live feed, seeing themselves being watched. Commenters referenced similar existing works, including the music video for Massive Attack's False Flags and Belgian artist Dries Depoorter, who uses AI to match open webcam footage with Instagram photos.

DIY Deployments and Legal Gray Areas One user shared an anecdote about building a DIY ALPR system to create a public "leaderboard" of speeding cars in their neighborhood. This sparked a debate on the legality of citizen-operated license plate readers. Commenters noted that states like California and Colorado have specific regulations (such as CA Civil Code 1798.90.5) that strictly control ALPR usage, potentially making private operation illegal despite the data being derived from public spaces.

Privacy vs. The First Amendment The legal discussion evolved into a constitutional debate. While some laws restrict the collection and analysis of ALPR data by private entities, users argued that because there is no expectation of privacy in public spaces, filming and processing that data should be protected by the First Amendment. Participants highlighted the tension and perceived hypocrisy in laws that allow law enforcement to utilize these massive tracking networks while simultaneously restricting private citizens from using the same technology on privacy grounds.

Future of Surveillance The discussion also touched on the concept of "democratized surveillance" akin to Vernor Vinge's novel Rainbows End, suggesting that rather than banning the tech, society might eventually move toward a model where all surveillance feeds are public domain to ensure accountability.

No AI* Here – A Response to Mozilla's Next Chapter

Submission URL | 415 points | by MrAlex94 | 238 comments

Waterfox’s founder announces a new website and uses the moment to take aim at Mozilla’s AI-first pivot. His core argument: LLMs don’t belong at the heart of a browser.

Key points

• Not all “AI” is equal: He’s fine with constrained, single‑purpose ML like Mozilla’s local Bergamot translator (clear scope, auditable outcomes). LLMs are different—opaque, hard to audit, and unpredictable—especially worrying when embedded deep in a browser.
• The “user agent” problem: A browser is supposed to be your agent. Insert an LLM between you and the web, and you’ve created a “user agent’s user agent” that can reorganize tabs, rewrite history, and shape what you see via logic you can’t inspect.
• Optional isn’t enough: Even if Firefox makes AI features opt‑in, users can’t realistically audit what a black box is doing in the background. The cognitive load of policing it undermines trust.
• Mozilla’s dilemma: With Firefox’s market share sliding and search revenue pressure mounting, Mozilla is chasing “AI browsers” and mainstream users—risking further alienation of the technical community that once powered its strength.
• Waterfox’s stance: Focus on performance, standards, and customization; no LLMs “for the foreseeable future.” A browser should be a transparent steward of its environment, not an inscrutable co‑pilot.

Why it matters As “AI browsers” proliferate (even Google reportedly explores a non‑Chrome browser), this piece articulates the counter‑thesis: trust, transparency, and user agency are the browser’s true moat—and LLMs may erode it.

Based on the discussion, the community response is mixed, shifting between technical debates about the nature of ML and practical anecdotes regarding feature utility.

The "Black Box" Hypocrisy A significant portion of the discussion challenges the author’s distinction between Mozilla’s "good" local translation tools and "bad" LLMs. Commenters argue that modern neural machine translation (NMT) is just as much a "black box" as an LLM.

• Verification: While Waterfox claims translation is auditable, users point out that NMT operates on similar opaque neural architectures. However, some conceded that translation has a narrower scope, making it easier to benchmark (e.g., verifying it doesn't mangle simple sentences) compared to the open-ended nature of generative agents.
• Manipulation Risks: One user hypothesized a "nefarious model" scenario where a translation tool subtly shifts the sentiment of news (e.g., making political actions seem more positive) or alters legal clauses. The consensus remains that for high-stakes legal work, neither AI nor uncertified human translation is sufficient.

The Utility of Summarization The debate moved to the practical value of having LLMs built into the browser, specifically for summarization:

• YouTube & Fluff: several users find AI essential for cutting through content spanning widely different signal-to-noise ratios, particularly 15-minute YouTube videos that contain only two sentences of actual substance.
• Low-Stakes Legalese: One user praised local LLMs for parsing ISP contracts—documents that are necessary to check but too tedious to read in full.
• Erosion of Skills: Counter-arguments were raised about the cognitive cost of convenience. Some users fear that relying on summaries will destroy reading comprehension and attention spans. Others argued that if an article is bad enough to need summarizing, it probably shouldn't be read at all.

Integration vs. External Tools While many see the utility in AI tools, there is resistance to the browser vendor forcing them upon the user. Some participants prefer using external tools (like Raycast or separate ChatGPT windows) to summarize content on their own terms, rather than having an "AI" browser interface that feels cluttered or intrusive.

I ported JustHTML from Python to JavaScript with Codex CLI and GPT-5.2 in hours

Submission URL | 205 points | by pbowyer | 117 comments

Simon Willison used agentic coding to straight‑port Emil Stenström’s pure‑Python HTML5 parser (JustHTML) to JavaScript in a single evening. Running GPT‑5.2 via Codex CLI with an autonomous “commit and push often” loop, he produced a dependency‑free library, simonw/justjshtml, that passes essentially the full html5lib-tests suite—demonstrating how powerful tests plus agents can be for cross‑language ports.

Highlights

• What he built: justjshtml — a no‑deps HTML5 parser for browser and Node that mirrors JustHTML’s API
• Test results: ~9,200 tests pass (tokenizer 6810/6810; tree 1770/1782 with a few skips; serializer 230/230; encoding 82/83 with one skip)
• Scale of output: ~9,000 LOC across 43 commits
• Agent run: ~1.46M input tokens, ~97M cached input tokens, ~625k output tokens; ran mostly unattended
• Workflow: Agent wrote a spec.md, shipped a “Milestone 0.5” smoke parse, wired CI to run html5lib-tests, then iterated to green
• Time: ~4–4.5 hours, largely hands‑off

Why it matters

• Validates a practical pattern: pair a rock‑solid test suite with an autonomous agent to achieve reliable, rapid ports of complex, spec‑heavy systems.
• Shows that fully‑tested, browser‑grade HTML parsing is feasible in plain JS without dependencies.

Based on the discussion, here is a summary of the comments:

The Power of Language-Agnostic Tests The central theme of the discussion was that the success of this project relied heavily on html5lib-tests—a comprehensive, implementation-independent test suite. Simon Willison and others noted that such "conformance test suites" are rare but act as a massive "unlock" for AI porting.

• Methodology: Users suggested a standardized workflow for future projects: treat the original algorithm as canonical, generating inputs/outputs to create a generic test suite (possibly using property-based tools like Hypothesis), and then using agents to build ports in other languages that satisfy those tests.
• Agent-Driven Testing: Some commenters proposed using agents to write the test suites first by analyzing code to maximize coverage, then asking a second agent to write an implementation that passes those tests.

Porting Experiences & Challenges Code translation isn't always seamless.

• Latent Space Translation: User Havoc shared an experience porting Python to Rust; the LLM failed until the original Python source was provided as context, allowing the model to pattern-match the logic effectively across languages.
• Bug-for-Bug Compatibility: Users noted that without a standardized external test suite, porting requires verifying "bug-for-bug" compatibility, which is difficult when moving between languages with different type systems or runtime behaviors.

Open Source Philosophy in the AI Era A debate emerged regarding the incentives of open source when AI can effortless port (or "steal") logic.

• Defensive Coding: One user (heavyset_go) mused about keeping test suites private to prevent easy forks or automated ports that undermine the original creator's ability to capture value.
• Counterpoint: Willison argued the opposite, suggesting that investing in language-independent test suites rapidly accelerates ecosystem growth and follow-on projects. Other commenters warned that hiding tests creates a hostile environment and undermines the collaborative spirit of open source.

Historical Parallel: Mozilla User cxr pointed out a fascinating parallel: Firefox’s HTML5 parser was originally written in Java and is still mechanically translated to C++ for the Gecko codebase. They noted that this pre-LLM approach validates the concept of maintaining a high-level canonical source and mechanically derived ports, which modern AI agents now make accessible to individual developers.

Show HN: TheAuditor v2.0 – A “Flight Computer” for AI Coding Agents

Submission URL | 30 points | by ThailandJohn | 8 comments

Auditor: a database-first static analysis tool to give AI (and humans) ground-truth context about your code

What’s new

• Instead of re-parsing files on every query, Auditor indexes your whole repo into a structured SQLite database, then answers queries from that DB. That enables sub‑second lookups across 100K+ LOC and incremental re-indexing after changes.
• It’s privacy-first: all analysis runs locally. Network features (dependency checks, docs fetch, vuln DB updates) are optional; use --offline for air‑gapped runs.
• Designed to be framework-aware, with 25 rule categories and 200+ detections spanning Python, JS/TS, Go, Rust, Bash, and Terraform/HCL. It tracks cross-file data flow/taint, builds complete call graphs, and surfaces architectural issues (hotspots, circular deps).

How it works

• Python: deep semantic analysis using the native ast module plus 27 specialized extractors (e.g., Django/Flask routes, Celery tasks, Pydantic validators).
• JavaScript/TypeScript: full semantic understanding via the TypeScript Compiler API (module resolution, types, JSX/TSX, Vue SFCs, tsconfig aliases).
• Go/Rust/Bash: fast structural parsing with tree-sitter + taint.
• Deterministic, database-backed queries (recursive CTEs) intended to be consumed by AI agents to reduce hallucinations. The project shows an A/B refactor test where the DB-first workflow prevented incomplete fixes.

Why it matters

• Traditional SAST and grep-y approaches can be slow, heuristic, or context-poor at scale. By front-loading indexing and storing code intelligence in SQL, Auditor turns codebase questions (callers, taint paths, blast radius) into quick, reliable queries—useful for both engineers and AI coding agents.

Notable commands

• aud full — full index
• aud query --symbol ... --show-callers --depth 3 — call graph queries
• aud blueprint --security — security overview
• aud taint --severity critical — taint flow findings
• aud impact --symbol ... — change blast radius
• aud workset --diff main..HEAD; aud full --index — incremental re-index

Trade-offs and limits

• Indexing prioritizes correctness over speed: expect ~1–10 minutes initially on typical repos.
• Highest fidelity for Python and JS/TS; Go/Rust are structural (no full type resolution). C++ not supported yet.
• Default mode makes some network calls; explicitly use --offline for strict local-only analysis.

Positioning

• Think CodeQL/Semgrep meets an LSP-grade semantic model, but with a persistent database optimized for fast, repeatable queries and AI integration—an “antidote to vibecoding” that favors verifiable context over guesswork.

Discussion Summary:

The discussion focuses heavily on performance comparisons with similar tools and the architectural decision to move beyond Tree-sitter for analysis.

• Performance vs. Depth: User jblls compared Auditor to Brokk, noting that Brokk is significantly faster (indexing ~1M LOC/minute). The creator (ThailandJohn) clarified that Auditor's speed depends on the depth of analysis: Python indexes at ~220k LOC/min, while Node/TypeScript is slower (~50k LOC/min) due to compiler overhead and framework extraction. The creator emphasized that Auditor prioritizes deep data flow and cross-file provenance over raw speed.
• Tree-sitter Limitations: Several users asked why the project uses a "pseudo-compiler" approach rather than relying solely on Tree-sitter. The creator explained that while Tree-sitter is incredibly fast, it is limited to syntax nodes and struggles with semantic tasks like cross-module resolution, type checking, and complex taint tracking (e.g., following function arguments). Early prototypes using Tree-sitter resulted in shallow analysis and excessive false positives, necessitating a move to the TypeScript Compiler API and Python’s native AST module to ensure accurate call chains and data flow.
• Miscellaneous: One user requested clarification on the project's license, while another noted a recent uptick in formal verification and static analysis tools appearing on Hacker News.

AIsbom – open-source CLI to detect "Pickle Bombs" in PyTorch models

Submission URL | 50 points | by lab700xdev | 35 comments

AI SBOM: scanning AI models for malware and license landmines

What it is

• AIsbom is a security and compliance scanner for ML artifacts that inspects model binaries themselves—not just requirements files.
• It parses PyTorch .pt/.pkl, SafeTensors .safetensors, and (new in v0.2.4) GGUF model files to surface remote code execution risks and hidden license restrictions.

Why it matters

• Model files can be executable: PyTorch checkpoints often contain Pickle bytecode that can run arbitrary code on load.
• License data is frequently embedded in model headers; deploying a “non‑commercial” model by mistake can create major legal exposure.

How it works

• Deep binary introspection of model archives without loading weights into RAM.
• Static disassembly of Pickle opcodes to flag dangerous calls (e.g., os/posix system calls, subprocess, eval/exec, socket).
• Extracts license metadata (e.g., CC‑BY‑NC, AGPL) from SafeTensors headers and includes it in an SBOM.
• Outputs CycloneDX v1.6 JSON with SHA256 hashes for enterprise tooling (Dependency‑Track, ServiceNow), plus an offline HTML viewer at aisbom.io/viewer.html.

CI/CD integration

• Ships as a GitHub Action to block unsafe or non‑compliant models on pull requests.

Getting started

• pip install aisbom-cli, then run: aisbom scan ./your-project
• Generates sbom.json and a terminal report of security/legal risks.
• Includes a test artifact generator to safely verify detections.

License and status

• Apache-2.0, open source, with the latest release adding GGUF scanning—useful for popular llama.cpp-style LLM deployments.

Bottom line

• AIsbom treats AI models as code and IP, bringing SBOM discipline to AI supply chains by catching RCE vectors and licensing pitfalls before they ship.

Discussion Summary

The author (lab700xdev) introduced AIsbom to address the "blind trust" developers place in massive binary model files accumulated from sources like Hugging Face. The ensuing discussion focused on the persistence of insecure file formats, the best methods for static analysis, and where security checks should live in the ML pipeline.

• The Persistence of Pickle: While users like yjftsjthsd-h noted that the ecosystem is moving toward SafeTensors to mitigate code execution risks, the OP argued that while inference tools (like llama.cpp) have adopted safer formats, the training ecosystem and legacy checkpoints still heavily rely on PyTorch's pickle-based .pt files, necessitating a scanner.
• Detection Methodology: Participants debated the efficacy of the tool's detection logic. User rfrm and fby criticized the current "deny-list" approach (scanning for specific dangerous calls like os.system) as a game of "whac-a-mole," suggesting a strict allow-list of valid mathematical operations would be more robust. The OP agreed, stating the roadmap includes moving to an allow-list model.
• Static Analysis vs. Fuzzing: User anky8998 (from Cisco) warned that static analysis often misses obfuscated attacks, sharing their own pickle-fuzzer tool to test scanner robustness. Others recommended fickling for deeper symbolic execution, though the OP distinguished AIsbom as a lightweight compliance/inventory tool rather than a heavy decompiler.
• Deployment & UX: User vp compared the current state of AI model downloading to the early, chaotic days of NPM, suggesting a "Right-click -> Scan" OS integration to reduce friction for lazy developers.
• Timing: The OP emphasized that scanning must occur in CI/CD (pre-merge) rather than at runtime; by the time a model is loaded for inspection in a live environment, the pickle bytecode has likely already executed, meaning the system is already compromised.

There was also a minor semantic debate over the term "Pickle Bomb," with some users arguing "bomb" implies resource exhaustion (like a Zip bomb) rather than Remote Code Execution (RCE), though the OP defended it as a colloquial term for a file that destroys a system upon loading.

8M users' AI conversations sold for profit by "privacy" extensions

Submission URL | 810 points | by takira | 243 comments

Headline: Popular “privacy” VPN extension quietly siphoned AI chats from millions

What happened

• Security researchers at Koi say browser extensions billed as privacy tools have been capturing and monetizing users’ AI conversations, impacting roughly 8 million users. The biggest offender they detail: Urban VPN Proxy for Chrome, with 6M+ installs and a Google “Featured” badge.
• Since version 5.5.0 (July 9, 2025), Urban VPN allegedly injected site-specific scripts on AI sites (ChatGPT, Claude, Gemini, Copilot, Perplexity, DeepSeek, Grok, Meta AI, etc.), hooked fetch/XMLHttpRequest, parsed prompts and responses, and exfiltrated them to analytics.urban-vpn.com/stats.urban-vpn.com—independent of whether the VPN was turned on.
• Captured data reportedly includes every prompt and response, conversation IDs, timestamps, session metadata, platform/model info. There’s no user-facing off switch; the only way to stop it is to uninstall the extension.

Why it matters

• People share extremely sensitive content with AI: medical, financial, proprietary code, HR issues. Auto-updating extensions flipped from “privacy” helpers to surveillance without notice.
• Google’s “Featured” badge and high ratings didn’t prevent or catch this, undermining trust in Chrome Web Store curation.

How it worked (high level)

• Extension watches for AI sites → injects per-site “executor” scripts (e.g., chatgpt.js, claude.js).
• Overrides network primitives (fetch/XMLHttpRequest) to see raw API traffic before render.
• Packages content and relays it via postMessage (tag: PANELOS_MESSAGE) to a background worker, which compresses and ships it to Urban VPN servers—presented as “marketing analytics.”

Timeline

• Pre–5.5.0: no AI harvesting.
• July 9, 2025: v5.5.0 ships with harvesting on by default.
• July 2025–present: conversations on targeted sites captured for users with the extension installed.

What to do now

• If you installed Urban VPN Proxy (or similar “free VPN/protection” extensions), uninstall immediately.
• Assume any AI chats since July 9, 2025 on targeted platforms were collected. Delete chat histories where possible; rotate any secrets pasted into prompts; alert your org if sensitive work data was shared.
• Audit all extensions. Prefer paid, vetted tools; restrict installs via enterprise policies; use separate browser profiles (or a dedicated browser) for AI work to limit extension exposure.

Bigger picture

• Extensions have kernel-level powers for the web. Auto-updates plus permissive permissions are a risky combo, and “privacy” branding is no shield.
• Stores need stronger runtime monitoring and transparency for code changes; users and orgs need a default-deny posture on extensions touching productivity and AI sites.

Summary of Discussion:

The discussion focuses heavily on the failure of browser extension store curation—specifically the contrast between Google and Mozilla—and the technical difficulty of identifying malicious code within updates.

Store Policies & Trust (Chrome vs. Firefox):

• The Value of Badges: Users expressed frustration that Google’s "Featured" badge implies safety and manual review, yet failed to catch the harvesting code. Some speculated that Google relies too heavily on automated heuristics because they "hate paying humans," whereas Mozilla’s "Recommended" program involves rigorous manual review by security experts for every update.
• Source Code Requirements: A key differentiator noted is that Google allows minified/obfuscated code without the original source, making manual review nearly impossible. In contrast, commenters pointed out that Mozilla requires buildable source code for its "Recommended" extensions to verify that the minified version matches the source.
• Update Lag: It was noted that this rigor comes at a cost: Firefox "Recommended" updates can take weeks to approve, while Chrome updates often push through in days (or minutes), allowing malicious updates to reach users faster.

Technical Challenges & Obfuscation:

• Hiding in Plain Sight: Users debated the feasibility of manual review, noting that even with access to code, malicious logic is easily hidden. One commenter demonstrated how arbitrary code execution can be concealed within innocent-looking JavaScript array operations (using .reduce and string manipulation) that bypasses static analysis.
• User Mitigation: Suggestions for self-protection included downloading extension packages (.xpi/.crx), unzipping them, and auditing the code manually. However, others countered that this is unrealistic for average users and difficult even for pros due to minification and large codebases (e.g., compiled TypeScript).
• Alternatives: Some users advocate for using Userscripts (via tools like Violentmonkey) instead of full extensions, as the code is generally smaller, uncompiled, and easier to audit personally.

Company Legitimacy:

• Corporate Sleuthing: Commenters investigated "Urban Cyber Security INC." Users found corporate registrations in Delaware and addresses in NYC, initially appearing legitimate. However, follow-up comments identified the addresses as virtual offices and coworking spaces, noting that "legitimate" paperwork costs very little to maintain and effectively masks the actors behind the software.

Show HN: Solving the ~95% legislative coverage gap using LLM's

Submission URL | 39 points | by fokdelafons | 21 comments

I don’t see a submission attached. Please share the Hacker News link or paste the article text (or a screenshot), plus any HN context like title, points, and comment count.

Preferences that help:

• Length: one-paragraph blurb or a 2–3 paragraph digest?
• Tone: neutral, punchy, or playful?
• Extras: include key takeaways or notable comments?

I can also handle multiple submissions if you’re compiling a daily digest.

Here is a daily digest summary based on the deciphered discussion:

Topic: AI for Legislative Analysis (Civic Projects) Context: A Show HN submission about a tool that uses LLMs to analyze and summarize government bills and laws.

Summary of Discussion The community expressed cautious optimism about applying LLMs to legal texts. The discussion was anchored by a notable anecdote from a user whose friend successfully used an LLM to identify conflicting laws in Albania’s legal code during their EU accession process. However, trust remained a central friction point; commenters questioned how the tool handles hallucinations and inherent political bias (citing specific geopolitical examples). The tool's creator (fkdlfns) acknowledged that while bias can’t be stripped entirely, they mitigate it by forbidding "normative language" in prompts and enforcing strict traceability back to source sections.

Key Comments:

• The "Killer App" Use Case: One user shared that reviewing laws by hand is tedious, but LLMs excelled at finding internal legal conflicts for a nation updating its code for the EU.
• The Bias Problem: A thread focused on whether LLMs can ever be neutral, or if they are "baked" with the political spin of their training data. The creator argued for using "heuristic models" rather than simple pattern matching to constrain editorial framing.
• Technical Issues: Several users reported the site was "hugged to death" (crashed by traffic) or blocked by corporate firewalls, likely due to domain categorization.

AI is wiping out entry-level tech jobs, leaving graduates stranded

Submission URL | 126 points | by cratermoon | 157 comments

AI is hollowing out entry-level tech jobs, pushing grads into sales and PM roles

• Rest of World reports a sharp collapse in junior tech hiring as AI automates debugging, testing, and routine maintenance. SignalFire estimates Big Tech’s intake of fresh grads is down more than 50% over three years; in 2024, only 7% of new hires were recent graduates, and 37% of managers said they’d rather use AI than hire a Gen Z employee.
• On the ground: At IIITDM Jabalpur in India, fewer than 25% of a 400-student cohort have offers, fueling campus panic. In Kenya, grads say entry-level tasks are now automated, raising the bar to higher-level system understanding and troubleshooting.
• Market data: EY says Indian IT services cut entry roles by 20–25%. LinkedIn/Indeed/Eures show a 35% drop in junior tech postings across major EU countries in 2024. The WEF’s 2025 report warns 40% of employers expect reductions where AI can automate tasks.
• Recruiters say “off-the-shelf” technical roles that once made up 90% of hiring have “almost completely vanished,” and the few junior roles left often bundle project management, customer communication, and even sales. Some employers expect new hires to boost output by 70% “because they’re using AI.”
• The degree gap: Universities are struggling to update curricula fast enough, leaving students to self-upskill. Some consider grad school to wait out the storm—only to worry the degree will be even less relevant on return.

While the article attributes the collapse in entry-level hiring primarily to AI automation, the Hacker News discussion argues that macroeconomic factors and corporate "optics" are the true drivers.

• Macroeconomics vs. AI: Many commenters view the "AI replaced them" narrative as a convenient scapegoat for post-COVID corrections and the end of ZIRP (Zero Interest Rate Policy). Users argue that companies are cutting costs to fund massive AI hardware investments and optimize stock prices, rather than actually replacing humans with software. One internal FAANG employee claimed junior roles are actually opening up again, dismissing contrary claims by CEOs like Marc Benioff as "salesmen" pushing a narrative.
• The "Junior" Pipeline Debate: A significant disagreement emerged over the trend of replacing juniors with AI agents. Critics argue this demonstrates a disconnect from the engineering process: juniors are hired as an investment to become seniors; replacing them with AI (which doesn't "grow" into a senior engineer) destroys the future talent pipeline. However, others noted that for non-Big Tech companies, this "investment" logic fails because juniors often leave for higher FAANG salaries as soon as they become productive (~2 years).
• Skills and Education: Several commenters shifted blame to the candidates themselves, suggesting that many recent grads "min-maxed" or cheated their way through CS degrees, viewing the diploma as a receipt for a high-paying job without acquiring the necessary fundamental skills to pass interviews.
• Conflicting Anecdotes: Reports from the ground were mixed. While some users confirmed they haven't seen a junior hire in over two years at their large corporations, others (specifically at smaller firms or specific FAANG teams) reported that hiring is active or recovering, suggesting the situation is uneven across the industry.

Show HN: Zenflow – orchestrate coding agents without "you're right" loops

Submission URL | 29 points | by andrewsthoughts | 15 comments

Zenflow: an AI-orchestration app for “spec-first” software development

What it is

• A standalone app (by “Zencoder”) that coordinates multiple specialized AI agents—coding, testing, refactoring, review, verification—to implement changes against an approved spec, not ad-hoc prompts.

How it works

• Spec-driven workflows: Agents read your specs/PRDs/architecture docs first, then implement via RED/GREEN/VERIFY loops.
• Built-in verification: Automated tests and cross-agent code review gate merges; failed tests trigger fixes.
• Parallel execution: Tasks run simultaneously in isolated sandboxes to avoid codebase conflicts; you can open any sandbox in your own IDE.
• Project visibility: Kanban-style views of projects, tasks, and agent activity; supports multi-repo changes with shared context.

Positioning and claims

• “Brain vs. engine”: Zenflow orchestrates and verifies, while “Zencoder” agents do the coding/testing.
• Aims to prevent “prompt drift” and “AI slop,” with the team claiming 4–10× faster delivery and predictable quality.
• Emphasizes running tens/hundreds of agents in parallel without stepping on each other.

Availability

• Desktop app available; Windows version “coming soon” with a waitlist.
• If download doesn’t start (e.g., due to tracking protection), they say you can grab it directly without signup.

Why it matters

• Pushes beyond single-assistant coding toward production-oriented, multi-agent orchestration with verification loops—an approach many teams are exploring to make AI output reliable at scale.

Zenflow: an AI-orchestration app for “spec-first” software development

The discussion emphasizes a shift from "magic prompts" to structured, spec-driven development, with users generally praising the application's execution while requesting more flexibility.

• Workflow & UX: Early testers complimented the onboarding process and UI, noting that the "spec-first" approach forces better planning and testing compared to ad-hoc prompting. The automation of mundane Git operations—handling branching, commits, and creating PRs with templated descriptions—was highlighted as a major productivity booster over juggling CLI commands.
• Model Flexibility: A recurring request was for "Bring Your Own Agent" support; users expressed a desire to plug in external models like Claude, Gemini, or Codex rather than being locked into Zencoder’s proprietary agents.
• Skepticism & Marketing: While some found the multi-agent orchestration impressively handled hallucinations, others critiqued the marketing language—specifically the use of the term "slop"—as buzzword-heavy. One user argued that "orchestration" often just disguises brittle, engineered prompts that fail when conditions change.
• Future Implications: The conversation touched on the theoretical trajectory of AI coding, with users predicting a return to formal or semi-formal verification methods to ensure agent outputs mathematically match specifications.
• Support: The thread served as a support channel, identifying a Firefox tracking protection bug that blocked downloads, which the creator addressed with direct links.

CC, a new AI productivity agent that connects your Gmail, Calendar and Drive

Submission URL | 16 points | by pretext | 9 comments

Google Labs is testing “CC,” an email-first AI agent that scans your Gmail, Calendar, and Drive to send a personalized “Your Day Ahead” briefing each morning.

Key points:

• Access: Waitlist open to US/Canada users (18+) with consumer Google accounts; “Google AI Ultra” and paid subscribers get priority. Requires Workspace “Smart Settings” enabled. Sign up at labs.google/cc.
• How it works: Interact entirely via email—message your-username+cc@gmail.com or reply to the briefing to teach, correct, or add to-dos. You can CC it on threads for private summaries. It only emails you, never others.
• Not part of Workspace/Gemini: It’s a standalone Google Labs experiment governed by Google’s Terms and Privacy Policy (Workspace Labs and Gemini privacy notices don’t apply).
• Data control: You can disconnect anytime. Important: deleting items from Gmail/Drive doesn’t remove them from CC’s memory—disconnect to fully clear CC data. Past emails remain in your inbox.
• Odds and ends: Mobile Gmail link issues are being fixed. Feedback via thumbs up/down in emails or labs-cc-support@google.com.

Why it matters: Google is trialing a low-friction, email-native AI assistant with tight Gmail/Calendar/Drive integration—but with notable data-retention caveats and limited early access.

Discussion Summary:

Commenters discussed the potential market impact of "CC," debating whether native integrations like this render AI wrapper startups obsolete. However, skepticism remains regarding Google's commitment, with some noting that because it is a Google Labs experiment, it may eventually be shut down, leaving room for independent competitors.

Other key talking points included:

• Privacy & Alternatives: Users compared Google’s data handling to Apple’s, with one commenter outlining how to build a similar "morning briefing" system using iOS Shortcuts and ChatGPT to avoid Google's ecosystem.
• Utility: Despite the frequent cynicism regarding AI on the forum, several users expressed genuine appreciation for the concept, noting the practical value of context-aware briefings for managing daily workflows.
• Scope: There were brief mentions of the desire to connect arbitrary IMAP accounts, rather than being locked solely into the Gmail ecosystem.

Linux computer with 843 components designed by AI boots on first attempt

Submission URL | 33 points | by whynotmaybe | 7 comments

AI-designed Linux SBC boots first try after a one‑week build

LA startup Quilter says its “Project Speedrun” used AI to design a dual‑PCB, 843‑component single‑board computer in a week, then booted Debian on first power‑up. Humans reportedly spent 38.5 hours guiding the process versus ~430 hours for a typical expert-led effort—a roughly 10x time savings.

What’s novel

• Workflow focus: The AI automates the error-prone “execution” phase of PCB design (between setup and cleanup), and can handle all three stages if desired.
• Not an LLM: Quilter’s system isn’t a language model; it plays an optimization game constrained by physics. It wasn’t trained on human PCB datasets to avoid inheriting common design mistakes.
• Ambition: CEO Sergiy Nesterenko says the goal is not just matching humans but surpassing them on PCB quality and speed.

Why it matters

• Faster iteration could compress hardware development cycles and lower barriers for new hardware startups.
• Offloading the grind may let engineers explore more designs and get to market sooner.

Caveats and open questions

• This is a single demo; independent replication and full design files would help validate claims.
• Manufacturing realities—DFM/DFT, EMI/EMC compliance, thermal behavior, yield, BOM cost, and supply chain—remain to be proved at scale.
• “Boots Debian” is a great milestone, but long-term reliability and performance under load are still untested.

Source: Tom’s Hardware on Quilter’s “Project Speedrun.”

Skepticism on "Human-Level" Quality and Time Estimates While the submission highlights a 10x speed improvement, users within the manufacturing and engineering space scrutinized the project's specific claims, questioning the baseline comparisons and the usability of the raw AI output.

• Disputed Baselines: Commenters argued that the "430 hours" cited for a typical human expert to design a similar board is a massive overestimate used to inflate the marketing narrative. One user noted that skilled engineers usually complete layouts of this complexity in nearly 40 hours—roughly the same time the "Speedrun" project utilized human guidance (38.5 hours).
• "Cleanup" or Rescue? A deep drive into the project files by user rsz suggests the reported "cleanup" phase actually involved the human engineer salvaging a flawed design. Specific technical critiques included:
  • Power Distribution: The AI routed 1.8V power rails with 2-mil traces (unmanufacturable by standard fab houses like JLCPCB/PCBWay and prone to brownouts), which the human had to manually widen to 15-mil.
  • Signal Integrity: The AI failed to properly length-match high-speed lines (treating them like "8MHz Arduino" signals), specifically mangling Ethernet traces across multiple layers.
• Clarifying the Workflow: Users pointed out that the AI did not generate the schematics or the fundamental computer architecture. The project utilized an existing NXP reference design (i.MX 8M Mini) and a System-on-Module (SoM) approach. The AI’s contribution was strictly the physical layout (placing and routing) based on those existing constraints.
• Supply Chain Utility: On a positive note, the discussion acknowledged the tool's ability to handle "supply chain hiccups." When components (like a specific connector or Wi-Fi module) went out of stock, the system allowed for instant constraint swaps and re-runs in parallel, a task that is typically tedious for humans.

Instacart's AI-Enabled Pricing Experiments May Be Inflating Your Grocery Bill

Submission URL | 19 points | by bookofjoe | 5 comments

Consumer Reports: Instacart is A/B-testing prices per shopper, with differences up to 23% per item

A Consumer Reports + Groundwork Collaborative investigation found that Instacart is running widespread, AI-enabled price experiments that show different customers different prices for the same grocery items—often without their knowledge. In coordinated tests using hundreds of volunteers, about 75% of checked products were priced differently across users, with per-item gaps from $0.07 to $2.56 and, in some cases, as high as 23%. The experiments appeared across major chains including Albertsons, Costco, Kroger, Safeway, Sprouts, and Target. An accidentally sent email referenced a tactic dubbed “smart rounding.”

Instacart confirmed the experiments, saying they’re limited, short-term, randomized tests run with 10 retail partners that already apply markups, and likened them to long-standing in-store price tests. CR says every volunteer in its study was subject to experiments. A September 2025 CR survey found 72% of recent Instacart users oppose differential pricing on the platform.

Why it matters: Opaque, individualized pricing for essential goods raises fairness and privacy concerns and risks sliding toward “surveillance pricing” driven by personal data—especially amid elevated food inflation. What to watch: disclosure/opt-outs, which retailers are involved, and whether regulators push for transparency rules.

Discussion Summary:

Commenters expressed skepticism regarding Instacart's pricing models, with one user noting that the higher pricing baselines established since 2020 are becoming permanent and difficult to revert. Comparisons were drawn to the travel industry, where agents have observed similar dynamic pricing tactics in which checking fares can actively drive up rates. Others criticized the fundamental cost of the service, suggesting that Instacart has become completely "divorced" from the concept of affordable groceries. Several links to previous and related discussions on the topic were also shared.

Joseph Gordon-Levitt wonders why AI companies don't have to 'follow any laws'

Submission URL | 34 points | by alexgotoi | 13 comments

Joseph Gordon-Levitt calls for AI laws, warns of “synthetic intimacy” for kids and a race-to-the-bottom on ethics

At Fortune’s Brainstorm AI, actor–filmmaker Joseph Gordon-Levitt blasted Big Tech’s reliance on self-regulation, asking, “Why should the companies building this technology not have to follow any laws?” He cited reports of AI “companions” edging into inappropriate territory with minors and argued that internal “ethics” processes can still greenlight harmful features. Meta pushed back previously when he raised similar concerns, noting his wife’s past role on OpenAI’s board.

Gordon-Levitt said market incentives alone will steer firms toward “dark outcomes” without government guardrails, and criticized the “arms race with China” narrative as a way to skip safety checks. That framing drew pushback in the room: Stephen Messer (Collective[i]) argued U.S. privacy rules already kneecapped domestic facial recognition, letting China leap ahead. Gordon-Levitt conceded some regulation is bad but urged a middle ground, not a vacuum.

He warned about “synthetic intimacy” for children—AI interactions he likened to slot machines—invoking psychologist Jonathan Haidt’s concern that kids’ brains are “growing around their phones,” with real physical impacts like rising myopia. He also attacked genAI’s data practices as “built on stolen content,” saying creators deserve compensation. Not a tech pessimist, he says he’d use AI “set up ethically,” but without digital ownership rights, the industry is “on a pretty dystopian road.”

Here is a summary of the discussion:

Regulatory Capture and the "Tech Playbook" Much of the discussion centers on a deep cynicism regarding Big Tech’s relationship with the government. Commenters argue that companies follow a standard "playbook": ignore laws and ethics to grow rapidly and cheaply, make the government look like the villain for trying to regulate popular services, and finally hire lobbyists to write favorable legislation. Users described this as “capital capturing the legislature,” noting that firms like Google and Microsoft are now so established and influential ("omnipotent") that it may be too late for effective external regulation.

JGL’s Personal Connection to OpenAI Several users contextualized Gordon-Levitt's comments through his wife, Tasha McCauley, who previously served on the OpenAI board. Commenters noted that she left the board when CEO Sam Altman was reinstated—a move driven by board members who did not trust Altman to self-regulate. Users suggested that Gordon-Levitt's skepticism of corporate "ethics processes" likely mirrors his wife's insider perspective that these companies cannot be trusted to govern themselves.

Data Scrapping and Rhetoric There was specific skepticism regarding the data practices of AI companies, with one user questioning whether the aggressive behaviors of AI crawlers (ignoring mechanisms like robots.txt) constitute a breach of terms or illegitimate access under laws like the Computer Misuse Act. Finally, regarding Gordon-Levitt's warnings about children, a user remarked that "think of the children" arguments are often deployed in biased contexts to force regulation.