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AI Submissions for Sun Dec 07 2025

I failed to recreate the 1996 Space Jam website with Claude

Submission URL | 517 points | by thecr0w | 421 comments

A developer tried to get Claude (Opus 4.1) to rebuild the iconic 1996 Space Jam homepage from a single screenshot plus the original image assets—and ran straight into the limits of today’s vision LLMs.

What happened

• Setup: Man-in-the-middle proxy captured Claude Code’s full tool use (Read/Write/Bash), prompts, and responses to audit what the model “thought” versus what it did.
• First attempts: The layout looked vaguely right (planets around the logo), but the orbital pattern was wrong. Claude confidently declared success anyway.
• Forced reasoning backfired: The model produced seemingly careful measurements in its analysis, then ignored them when generating HTML/CSS.
• Hard limitation exposed: Pressed for specifics, Claude admitted it can’t extract exact pixel coordinates or measure precise distances from an image—only estimate. Confidence of matching within 5px: 15/100. $1,000 bet it matched exactly: “Absolutely not.”
• Corrections: The author initially assumed absolute positioning; commenters noted the original used tables.
• Tooling to help the model: Built grid overlays, labeled coordinate references, a color-diff that ignored the black background, and an auto-screenshot loop to reduce false positives and lock in pixel alignment.

Why it matters

• Vision LLMs remain fuzzy instruments: good at gestalt layout, bad at pixel-precise reconstruction.
• Self-critique ≠ adherence: Even when a model articulates the right plan, its code may diverge.
• Verification and external tools are essential: Deterministic measurement, diffs, and tight feedback loops beat “try harder” prompting.
• The nostalgic twist: Recreating a table-era site surfaced modern AI’s surprising blind spots.

Bonus: Someone else did manage a faithful recreation; the post links to that success. HN discussion is lively on model limits, measurement, and when to reach for traditional computer vision/OCR instead.

The Technical Truth: Tables and Spacer GIFs The discussion opened with a critical correction to the author's premise: the original Space Jam website didn’t use absolute positioning (which wasn't standard then), but relied on HTML tables and spacer GIFs (1x1 transparent pixels used to force width/height). Users pointed out that trying to recreate the site using modern CSS constructs ignores the "slicing" technique used in the 90s, where tools like Photoshop and Dreamweaver would split an image into a grid of table cells.

Nostalgia and Rendering Nightmares The thread evolved into a nostalgic trip through 1996 web development:

• The "Slicing" Era: Commenters recalled how entire user interfaces were drawn in 2D in Photoshop and then "spat out" as complex HTML tables glued together.
• Netscape Woes: Users shared war stories about nested tables crashing browsers or causing massive rendering delays in Netscape Navigator, where a missing closing tag or deep nesting (12+ levels) would result in a blank page for minutes.
• Hacker News Itself: A commenter noted the irony that Hacker News still uses nested tables for its comment threading. The shrinking text size on mobile for deep threads was historically a side effect of browser rendering logic for nested tables.

LLM Limitations and Hallucinations The consensus on Claude’s failure was that the model fell into a "people pleaser" trap. By trying to satisfy the author's request for code based on "constraints that didn't exist" (absolute positioning for that specific look), the AI hallucinated a solution rather than recognizing the historical context (table layouts).

• One user noted that LLMs struggle to say "I don't know" or "That premise is wrong," preferring to produce broken code over admitting defeat.
• Others argued that asking a text-based model to perform pixel-perfect spatial reasoning is currently outside the capabilities of the architecture, regardless of the prompt strategy.

Sidebar: CSS vs. Tables A sub-discussion debated whether modern CSS is actually "better" for layout than the old table methods, with some users joking that display: table and centering a div in CSS remain unnecessarily difficult compared to the brute-force simplicity of the 90s methods.

Bag of words, have mercy on us

Submission URL | 273 points | by ntnbr | 291 comments

Core idea: We keep misreading large language models because we treat them like people. Instead, think of them as a gigantic “bag of words” that returns the most statistically relevant words to whatever you toss in.

Key points

• Humans are wired to anthropomorphize, so LLM outputs trigger social instincts (theory of mind, intent, deception), which misleads us.
• “Bag of words” metaphor: an LLM has ingested an enormous corpus; it predicts plausible continuations. Apologies, confidence, and “lies” are just patterns from regions of that corpus, not intentions.
• Capability heuristic: it’s strong where the bag is dense (well-documented facts, common tasks), weak where it’s sparse (obscure taxonomy, niche trivia) or where truth requires grounding, counting, or reasoning beyond text.
• Broad, philosophical prompts yield platitudes because most human text on those topics is platitudinous.
• Treating AI as an all-seeing intellect leads to bad inferences (e.g., “even ChatGPT can’t explain this magic trick” doesn’t prove profundity; it just reflects gaps or guarded knowledge).
• Companies also “add invisible words” (system prompts, retrieval) to nudge better outputs—further evidence this is corpus steering, not mind-reading.

Why it matters

• Calibrates expectations: expect plausible text, not intent or reliability; verify facts.
• Guides usage and product design: use retrieval for sparse domains, constrain tasks, and measure performance by data coverage, not perceived “intelligence.”
• Deflates both hype and panic that come from projecting human psychology onto statistical text models.

Memorable examples: pareidolia (faces in toast), LLMs beating you at Go but miscounting r’s in “strawberry,” and confidently recommending glue on pizza—each a reminder: it’s patterns in text, not a person.

Discussion Summary The comment section debated the philosophical and technical validity of the "bag of words" reductionism, with users clashing over whether human cognition differs fundamentally from statistical prediction.

Mechanisms vs. "Magic" A central conflict arose over physical materialism. User kbldfryng challenged the notion that LLMs are incapable of abstract thought while human brains are, arguing that since brains aren't "magic," both are simply mechanisms. thsz countered with a deep dive into neurobiology, arguing that the brain's complexity—involving DNA, chemical structures, and potentially quantum effects—is magnitudes higher than current neural networks. Others, like dnlbln, rebutted with the functionality argument: "We didn't understand bird physiology to build a bird... we built planes."

Prediction as Thinking Several users questioned the distinction between "predicting words" and "thinking."

• Human Prediction: User blf argued that humans also act by predicting outcomes based on expectations, suggesting that "predicting the next token" might not be irrelevant to how minds actually work.
• Internal Models: ACCount37 and trvrsd noted that to predict effectively, LLMs build internal representations (embeddings) that act as a "world model," meaning they aren't just retrieving words but translating concepts.
• The Dice Analogy: nkrsc and others offered skepticism, comparing LLMs to shaking a cup of dice: the output may be a number, but the shaking process isn't "thinking."

Embodiment and Learning The comparison between training models and raising children sparked debate. While d-lsp argued that human intelligence is distinct because it is grounded in physical survival and embodiment rather than text ingestion, lstms amusingly noted that children often behave like LLMs—hallucinatory, emotional, and prone to repeating inputs until their brains mature.

Conclusion While some agreed with the author that we shouldn't anthropomorphize statistical models, a significant faction argued that dismissing LLMs as "just prediction" ignores the possibility that prediction is the foundational mechanic of intelligence itself.

Nested Learning: A new ML paradigm for continual learning

Submission URL | 139 points | by themgt | 10 comments

Google Research proposes “Nested Learning,” a continual-learning paradigm that treats a model not as one monolithic learner but as a stack of smaller, nested optimization problems—each with its own “context flow” and update frequency. The authors argue architecture and optimizer are two levels of the same thing, and that giving components different time scales of plasticity (like the brain) can mitigate catastrophic forgetting.

What’s new

• Multi-level learning: Components (e.g., attention, memory, even backprop itself) are framed as associative memory modules that learn at different rates. These are ordered into “levels” via update frequency.
• Unifying view: Training rules and network structure are seen as the same object at different depths, adding a new design dimension: where and how often each part learns.
• Deep optimizers: Reinterpreting optimizers (e.g., momentum) as learnable associative memories; replacing simple dot-product similarity with standard losses (e.g., L2) to make updates more robust to interference across samples.

Claims and early results

• A proof-of-concept, self-modifying architecture (“Hope”) reportedly beats SOTA on language modeling and manages long-context memory better.
• Transformers and memory modules are recast as (essentially) linear layers with different update frequencies, enabling multi-time–scale updates that reduce forgetting.

Why it matters

• Continual learning without catastrophic forgetting is a core blocker for self-updating LLMs. If parts of a model can learn on different time scales, you can acquire new skills while preserving old ones—potentially without heavy rehearsal buffers or brittle architectural hacks.

How it compares (at a high level)

• Related ideas include fast/slow weights, meta-learning, bilevel optimization, learned optimizers, hypernetworks, and memory-augmented models. Nested Learning tries to subsume these under a single optimization-centric lens rather than adding bespoke modules.

Open questions for readers

• Benchmarks and rigor: Which continual-learning suites and long-context tasks were used? How big are the gains and on what scales?
• Stability/cost: Does multi-time–scale updating introduce optimization instability or significant compute overhead?
• Practicality: Can this plug into existing training stacks? Any trade-offs versus retrieval-based memory or rehearsal methods?
• Availability: Paper, code, and reproducibility details?

TL;DR: Treat the network and its training rule as one nested system with components that learn at different speeds. That extra “depth” in where learning happens may curb catastrophic forgetting; an early “Hope” model shows promising long-context and LM results. Worth watching for concrete benchmarks and releases.

Discussion Summary:

The discussion focused on the practical implementation of the "Nested Learning" paradigm and the validity of its claims regarding continual learning.

• Reproduction and Architecture: Users identified a community attempt to reproduce the paper on GitHub. One commenter (NitpickLawyer) theorized that a practical implementation would likely involve freezing a pre-trained Transformer backbone (embeddings, attention blocks, and layer norms) to provide stable representations, while training the specific memory pathways (HOPE, TITAN, and CMS) as adapter-style layers. This approach was praised as a potentially revolutionary way to apply architectural enhancements to existing models without discarding previous training efforts.
• Skepticism: Some participants expressed confusion and doubt. User hvymmry questioned whether the paper was simply "gradient descent wrapped in terminology," asking for clarification on how freezing a model and adding nested components fundamentally solves catastrophic forgetting in practice.
• Context: pnrchy noted that the concept of heterogeneous architectures—where a meta-network optimizes specific tasks—has felt "self-evident" since 2019, implying the field has been moving toward this direction for time.
• Resources: A link was provided to a video by author Ali Behrouz explaining the concept as part of a NeurIPS 2025 presentation.

(Note: A significant portion of the distinct conversation was a tangent regarding an unrelated NVIDIA paper combining diffusion and autoregression, triggered by the similar naming conventions.)

Google Titans architecture, helping AI have long-term memory

Submission URL | 556 points | by Alifatisk | 177 comments

Google Research: Titans + MIRAS bring true long-term memory to AI by learning at inference time

• What’s new: Google introduces Titans (an architecture) and MIRAS (a theoretical framework) to let models update their own long‑term memory while they’re running. Goal: RNN‑like speed with transformer‑level accuracy on massive contexts.

• Why it matters: Transformers slow down quadratically with context length; linear RNNs/SSMs are fast but bottlenecked by fixed‑size states. Titans adds a much more expressive long‑term memory (a deep MLP) that can be updated on the fly, aiming for fast, scalable full‑document or streaming understanding without offline retraining.

• How it works:

  • Two memories: attention for precise short‑term recall; a neural long‑term memory (MLP) that compresses and synthesizes the past, whose summary is fed back into attention.
  • Surprise‑gated updates: the model uses its own gradient magnitude as a “surprise” signal to decide when to commit new information to long‑term memory.
  • Momentum and forgetting: it smooths surprise over recent tokens (to catch follow‑ups) and uses adaptive weight decay as a forgetting gate to manage capacity.
  • MIRAS unifies sequence models as associative memory systems, framing design choices like memory architecture and attentional bias (and related axes) under one blueprint.

• The HN angle: inference‑time learning/test‑time memorization without fine‑tuning, a potential path past context windows; blends the “RNNs are back” efficiency trend with transformer strengths.

• Open questions: stability and safety of on‑the‑fly parameter updates, interference vs. retention over very long streams, serving complexity and latency, and how results compare on standard long‑context benchmarks.

Papers: Titans and MIRAS are linked from the post.

The Discussion:

• Research vs. Replica: A major thread of criticism focuses on Google publishing theoretical papers without releasing code or weights. Commenters contrast this with the ecosystems around Meta (Llama) and DeepSeek, where "open" often means usable. Users express frustration that while the architecture is 11 months old in concept, the lack of an official implementation makes it difficult to verify performance against authorized baselines like Mamba or existing Transformers.
• The "Google Paradox": The discussion reignites the trope that Google excels at inventing core technologies (like the original Transformer, Hadoop equivalents, or Docker-style containers) but fails to productize them effectively. Skeptics suggest these papers often serve internal promotion metrics ("performance review engineering") rather than signaling an actual product shift, though some speculate that Gemini 3 may already be utilizing this architecture under the hood.
• The Scaling Wall: Several users point out the "path dependency" problem: it is nearly impossible for independent researchers to verify if Titans is actually superior to Transformers without access to massive compute for scaling. There is a sense that new architectures are validated only by those with the budget to train 30B+ parameter models, making the paper theoretically interesting but practically unverifiable for the broader community.
• Product Design vs. Model Sinking: A sidebar discussion argues that the industry is focusing too heavily on sinking capital into model benchmarks rather than product design. The argument is that long-term memory is useful, but the "winner" will likely be whoever builds focused, specific tools that solve user problems, rather than just raw general-purpose reasoning engines.

Using LLMs at Oxide

Submission URL | 682 points | by steveklabnik | 268 comments

Oxide’s Bryan Cantrill has a values-first playbook for how the company will use LLMs—less a static policy, more a rubric for judgment as the tech shifts.

What guides usage (in priority order):

• Responsibility: Humans own the work. Using an LLM never dilutes personal accountability for code, docs, tests, or prose.
• Rigor: LLMs should sharpen thinking, not replace it with auto-generated fluff.
• Empathy: There’s a human on the other end of every sentence—write and read with that in mind.
• Teamwork: Don’t erode trust. Simply disclosing “AI was used” can become a crutch that distances authors from their work.
• Urgency: Speed matters, but not at the expense of the above.

Where LLMs shine (and don’t):

• As readers: Excellent at instant comprehension, summarizing, and targeted Q&A over long docs (even good at spotting LLM-written text). Privacy matters: hosted tools often default to training on your uploads—watch those settings and euphemisms like “Improve the model for everyone.”
• As editors: Useful late in the process for structure and phrasing. Beware sycophancy and being steered off your voice if used too early.
• As writers: The weakest use. Output tends to be cliché-ridden with recognizable tells—embarrassing to savvy readers and corrosive to trust and responsibility.

A key caution: don’t use LLMs to dodge socially expected reading (e.g., evaluating candidate materials). The throughline: treat LLMs as potent tools for comprehension and critique, not as a substitute for your own judgment, voice, and ownership.

Discussion Summary

The discussion centers on the tension between engineering craftsmanship and the practical utility of LLMs, with specific anxiety regarding skill development for junior developers.

• Junior Engineers and Skill Acquisition: Commenters expressed concern that while senior engineers (like Cantrill) have the deep experience to use LLMs as a "force multiplier," junior engineers might use them as a crutch, bypassing the struggle necessary to build fundamental intuition. Users debated whether juniors need to "memorize multiplication tables" (syntax and boilerplate) or if LLMs simply remove the drudgery of tasks like data imports and messy parsing, allowing focus on higher-level logic.
• The Dreamweaver Analogy: A significant portion of the thread drew parallels between LLMs and early WYSIWYG editors like Adobe Dreamweaver or Microsoft FrontPage. Just as those tools lowered the barrier to entry but produced bloated, unsemantic HTML that professionals had to clean up, users fear LLMs are generating "good enough" code that is verbose, hard to maintain, and riddled with subtle bugs.
• Craft vs. Factory: The conversation highlighted a divide between "craftsmen" (who value clean, maintainable, understanding-based code) and specific "factory" contexts (agencies or startups where speed and "shipped" status outweigh code quality).
• Validation Mechanisms: Several users noted that LLMs excel in areas with unambiguous validation mechanisms—such as generating regex, security POCs, or strictly defined data schemas—where the output works or it doesn't. They struggle, however, in areas requiring architectural judgment or nuance, where verifying the output can be more mentally taxing than writing the code from scratch.

Over fifty new hallucinations in ICLR 2026 submissions

Submission URL | 487 points | by puttycat | 399 comments

GPTZero claims 1 in 6 ICLR 2026 submissions it scanned contain fake citations, and reviewers mostly missed them

• What happened: GPTZero ran its Hallucination Check on 300 ICLR 2026 submissions on OpenReview and found 50 with at least one “obvious hallucination” in the references. Many of those papers had already been reviewed by 3–5 experts who didn’t flag the issue; some carried average scores of 8/10 (normally accept). ICLR policy says a single clear hallucination can be an ethics violation leading to rejection.
• What “hallucination” looked like: fabricated coauthors on real papers, nonexistent references, wrong venues/years/titles, bogus or mismatched arXiv IDs. GPTZero posted a table of 50 human-verified examples.
• Scale: They scanned 300 of ~20,000 submissions and estimate “hundreds” more will surface as they continue. They’re also taking suggestions for specific papers to check.
• Why it matters: If accurate, even top-tier venues are struggling to catch LLM-induced sloppiness or fabrication in citations, adding pressure to an already overloaded peer-review pipeline and risking contamination of the scholarly record.
• Caveats: GPTZero sells detection tools (conflict of interest), the sampling method isn’t clear, and the false-positive rate isn’t reported. Some flagged issues (e.g., partially wrong author lists) may reflect sloppy citation rather than wholesale fabrication. Final acceptance decisions are still pending.

Here is a summary of the discussion:

Is this fraud or just the new normal? While most commenters agreed that hallucinated citations constitute "gross professional misconduct," several users, including mike_hearn, argued that academic citations were already broken. They pointed to the pre-LLM prevalence of "citation bluffing" (citing real papers that do not actually support the claim) and "non-reading," suggesting that LLMs are merely accelerating an existing crisis of integrity and sloppiness in scientific literature.

The burden on reviewers Self-identified reviewers noted that the peer-review system relies heavily on a presumption of good faith. User andy99 explained that reviewers act as "proofreaders checking for rigor," not private investigators; verifying every single reference manually is untenable given current workloads. Others argued that if a "single clear hallucination" is grounds for rejection, tools like GPTZero or other LLM-based checkers are becoming necessary infrastructure, much like syntax checkers.

The "Carpenter" Analogy User thldgrybrd offered a popular analogy: A carpenter who builds a shelf that collapses because they used their tools incorrectly is simply a "bad carpenter." Similarly, a scientist who uses an LLM to generate text and fails to catch fabricated data is guilty of negligence and is effectively a "bad scientist," regardless of the tool used.

Debate on demographics and bias A contentious thread emerged regarding the cultural origins of the submissions. Some users attempted to link the fraud to "low-trust societies" or specific nationalities (referencing Middle Eastern or Chinese names). This was met with strong pushback from others who pointed out that ICLR submissions are double-blind (reviewers cannot see author names). Furthermore, users noted that the "names" visible in the GPTZero examples were often part of the hallucinations themselves, not the actual authors of the paper.

Summary of Sentiment: The community sees this as a symptom of "lazy" science meeting powerful tools. While there is sympathy for overloaded reviewers, the consensus is that using AI to fabricate the scholarly record is an ethical breach that requires new automated detection methods, as human oversight is no longer sufficient.

OpenAI disables ChatGPT app suggestions that looked like ads

Submission URL | 67 points | by GeorgeWoff25 | 55 comments

OpenAI disables ChatGPT “app suggestions” after users mistake them for ads

• What happened: Paying ChatGPT users reported prompts that looked like ads for brands like Peloton and Target. OpenAI says these were experimental suggestions to surface third‑party apps built on the ChatGPT platform—not paid placements—but conceded the rollout “felt like ads.”

• OpenAI’s response: Chief Research Officer Mark Chen apologized, saying the team “fell short,” has turned off the feature, and will improve precision and add controls so users can dial suggestions down or off. ChatGPT head Nick Turley said there are “no live tests for ads” and that any screenshots weren’t advertisements.

• Context: Speculation about an ads push grew after OpenAI hired Fidji Simo as CEO of Applications. But a reported “code red” from CEO Sam Altman prioritizes core ChatGPT quality over new initiatives like advertising.

Why it matters:

• Blurred lines between recommendations and advertising can quickly erode user trust—especially among paying subscribers.
• Clear labeling, opt‑outs, and precision targeting will be essential if AI assistants surface third‑party experiences.
• Signals a near‑term strategic pivot toward product reliability over monetization experiments.

Summary of Discussion:

The discussion on Hacker News reflects deep skepticism regarding OpenAI’s claim that these were merely "app suggestions," with the majority of commenters viewing the move as the inevitable arrival of advertising on the platform.

Skepticism of the "Not-an-Ad" Defense

• Commenters overwhelmingly rejected the distinction between "app suggestions" and advertisements. Many argued that regardless of technical semantics, unwanted commercial prompts for third-party brands (like Peloton) constitute advertising.
• Users pointed out that "suggestion" features often function as the groundwork for ad infrastructure, suspecting that OpenAI is testing the technical plumbing for a future ad network while publicly denying it.
• The specific suggestion of Peloton drew mockery, with users criticizing the relevance of the brand and noting its declining stock performance, further fueling the perception that this was a paid placement rather than a useful organic suggestion.

Erosion of Trust and "Enshittification"

• There is significant distrust regarding OpenAI’s transparency. Comments described the executive response ("we fell short") as empty corporate platitudes and expressed doubt regarding the statement that there are "no live tests for ads."
• The community fears a rapid "enshittification" of the platform. Drawing comparisons to Google Search and streaming services (Netflix), users argued that high utility usually degrades into ad-bloat over time.
• A major concern is "Chatbot Optimization"—the idea that future answers will be biased toward paying brands rather than factual accuracy, rendering the tool less useful for information retrieval.

Monetization of Paid Tiers

• A heated debate emerged regarding the sanctity of paid subscriptions. While some users felt betrayed that a paid service would show ads, others argued that the "paid-plus-ads" model is the new industry standard (referencing streaming services).
• Commenters noted that the high inference costs of LLMs make ads inevitable, even for subscribers. Some speculated that OpenAI’s "vertical integration" of apps is simply a way to monetize their highly valuable, high-income user base.

Privacy and Profiling

• Users highlighted the unique danger of ads in LLMs, noting that ChatGPT builds detailed psychometric profiles and fingerprints of its users. This makes the potential for targeted manipulation much higher than in traditional search or social media advertising.

AI Submissions for Sat Dec 06 2025

Touching the Elephant – TPUs

Submission URL | 181 points | by giuliomagnifico | 52 comments

This deep dive argues that Google’s TPU isn’t magic—it’s a decade of ruthless, full-stack co-design tuned to one thing: linear algebra for neural nets. Spurred in 2013 when Google realized it would need to double datacenter capacity to meet AI demand, the team built a domain-specific accelerator in just 15 months. Twelve years later, TPU v7 “Ironwood” scales to 9,216 chips per pod delivering 42.5 exaflops at 10 MW. The piece contrasts the TPU’s focus with NVIDIA’s general-purpose GPU legacy, and situates TPUs within the post-Moore/Dennard era: when free performance ended, specialization became the path forward.

Key points:

• TPU’s edge comes from specializing for matrix multiplies and elementwise ops that dominate neural nets, exploiting favorable compute-to-memory scaling (O(n^3) vs O(n^2)).
• Neural networks’ predictability enables ahead-of-time execution planning, further justifying fixed-function silicon.
• Despite extensive public research, TPUs remained datacenter-only, creating an asymmetry: well-documented, but without a true external counterpart.
• The story is trade-offs over mystique: a deliberate hardware–software–systems co-design responding to stalled CPU scaling and exploding AI workloads.
• Context: alongside players like Groq, Amazon, and Tenstorrent, TPU stands as the original existence proof for modern AI accelerators, while NVIDIA deserves credit for catalyzing deep learning’s GPU era.

Why it matters: As AI models and training clusters keep ballooning, general-purpose compute hits limits. This essay explains why hyperscalers are betting on tightly targeted silicon—and how Google’s early, sustained commitment to TPUs became a strategic moat.

Here is a summary of the story and the discussion surrounding it.

Touching the Elephant – TPUs: Understanding Google’s Tensor Processing Unit This deep dive explores the history and architecture of Google’s TPU, framing it not as a "magic" solution, but as the result of a decade-long, ruthless hardware-software co-design focused entirely on linear algebra. Triggered by a 2013 realization that existing data centers couldn't meet projected AI demand, Google built a domain-specific accelerator that stripped away general-purpose features in favor of raw matrix math performance. The piece highlights the TPU v7 "Ironwood," capable of massive scale, and contrasts Google’s "ahead-of-time" static scheduling approach with NVIDIA’s dynamic GPU legacy. It argues that as Moore’s Law slows, such extreme specialization is the only path left for scaling AI compute.

Discussion Summary The discussion thread focuses heavily on architectural comparisons to historical processor failures and the geopolitical anxieties surrounding chip manufacturing.

• VLIW and the Itanium Comparison: A major technical thread draws parallels between the TPU’s reliance on the XLA (Accelerated Linear Algebra) compiler and Intel’s Itanium processors, which used Very Long Instruction Word (VLIW) architectures. Commenters note that while Itanium failed because general-purpose software is too unpredictable for static scheduling, TPUs succeed because neural network workloads are highly regular and predictable. This allows the compiler to manage memory and execution units explicitly, avoiding the complex "juggling" required by modern CPUs.
• Geopolitics and Manufacturing: Discussion shifted to reports that Chinese entities have acquired or replicated TPU designs (referencing Department of Justice indictments). However, users argued that possessing architectural blueprints is distinct from the ability to manufacture the chips. Several commenters described modern semiconductor fabrication (specifically at TSMC) as a "dark art" that cannot be easily replicated, suggesting that China's fabrication capabilities still lag behind the necessary cutting edge despite access to stolen IP.
• Lock-in vs. Performance: Users noted the trade-off inherent in the technology: while TPUs offer impressive scaling and dedicated performance, they effectively lock users into Google Cloud Platform (GCP). This was contrasted with NVIDIA’s CUDA moat, with some suggesting that while hardware designs can be stolen or replicated, the software ecosystem remains the harder barrier to overcome.
• Moore’s Law Debate: A side discussion challenged the article's premise that Moore’s Law is dead, calculating that transistor counts have stayed on track with 1965 predictions (citing the Apple M1 Ultra), though the cost and utility of those transistors in general-purpose computing remains debated.

Running Claude Code in a loop to mirror human development practices

Submission URL | 42 points | by Kerrick | 9 comments

• What it is: A CLI that runs Claude Code in a loop with persistent context, turning one-shot code edits into an iterative, self-improving workflow. The author built it to push a huge codebase from 0% to 80%+ unit-test coverage on a deadline.

• How it works:

  • A bash “conductor” repeatedly invokes Claude Code.
  • Each iteration creates a branch, generates a commit, opens a PR, waits on CI and reviews, then merges on success or closes on failure.
  • Context continuity comes from a single shared markdown file (e.g., TASKS.md) where the agent leaves concise notes and next steps, enabling baton-passing between runs.

• Why it’s different: Most AI coding tools stop after a single task and don’t retain memory. Here, persistent external memory plus GitHub workflows (PRs, CI, code owners) create a feedback loop that lets the agent tackle larger, multi-step work.

• “Wasteful but effective”: Failed PRs get discarded, but the agent learns from failures via CI output and its notes. The author argues this stochastic, idempotent approach works as costs drop—akin to running many small agents and trusting the overall distribution to move in the right direction.

• Integrations and ideas:

  • Schedule runs or trigger on events; respects existing repo policies.
  • Parallel “specialized agents” (dev, tests, refactoring) to divide work in monorepos—though coordination can be tricky.
  • Dependabot on steroids: not just updating deps, but iteratively fixing breakages until CI is green.
  • Suited for big refactors (e.g., modularizing a monolith, async/await migrations, style overhauls).

• Real-world glimpse: The markdown memory enabled self-directed behavior like “run coverage → pick lowest-coverage file → improve → leave notes,” reducing context drift and looping.

• Caveats:

  • Can be compute/token heavy; risk of PR noise if not throttled.
  • Requires careful prompting to keep notes terse and actionable.
  • “Dangerously skip permissions” and auto-merge need governance to avoid unsafe changes.
  • Coordination overhead increases with multiple agents.

• Big picture: Moves AI coding from single-shot assistants toward continuous, CI-integrated agents with explicit memory—closer to a dependable “agent-in-the-loop” development model.

Discussion Summary:

Ideally suited for a submission about brute-forcing unit test coverage, the commentary focuses heavily on the distinction between quantity and quality.

• The "BS" Factor: While yellow_lead admits to using similar methods to hit contractual 80% coverage targets on massive legacy codebases, grnvcd warns that left to its own devices, Claude tends to write "plausible-looking BS" that struggles with stateful, real-world systems.
• The Review Bottleneck: ParanoidShroom notes that while they have used similar scripts for weeks, the process is exhausting because humans still have to spend hours reviewing the output to ensure validity. botanical76 adds that writing good tests usually involves an iterative process (introducting bugs to verify the test fails properly), which becomes prohibitively expensive in terms of time and tokens when done via AI.
• The "Ralph Wiggum" Technique: CharlesW points out that this specific pattern—stubborn persistence despite setbacks—is amusingly referred to as the "Ralph Wiggum" technique in Anthropic’s own plugin repository.

YouTube caught making AI-edits to videos and adding misleading AI summaries

Submission URL | 401 points | by mystraline | 222 comments

YouTube is quietly A/B-testing AI retouching on some creators’ videos—without telling them or viewers. Musicians Rick Beato (5M+ subs) and Rhett Shull noticed their faces and details looked subtly “off” (smoother skin, sharper folds, even slightly altered ears). After they spoke up, YouTube’s creator liaison Rene Ritchie confirmed a “small experiment” on select Shorts using machine learning to clarify, denoise, and improve video quality—likening it to smartphone processing.

Why it matters

• Consent and disclosure: Edits are happening post-upload and pre-distribution, without creator approval or labels. Critics argue that’s a hidden layer of manipulation distinct from visible filters.
• Trust and authenticity: Even minor, unannounced retouching can undermine audience trust—especially for news, education, and informational content.
• Creep of AI pre-processing: Follows broader industry trends (e.g., Samsung’s AI-boosted moon photos, Google Pixel’s Best Take), normalizing AI-altered media by default.

Creator reactions

• Rhett Shull: Says it “looks AI-generated” and worries it erodes trust.
• Rick Beato: Notes it felt unnatural but remains broadly supportive of YouTube’s experimentation.

Open questions

• Scope: Is this limited to Shorts or also affecting standard uploads? How widespread is the test?
• Controls: Will YouTube provide opt-out/opt-in toggles and visible “AI-enhanced” labels?
• Policy and regulation: How this fits with transparency requirements and platform policies on synthetic or altered media.

Bottom line: YouTube admits to a limited test of AI-driven “clarity” enhancements on Shorts, but doing it silently has sparked a debate over consent, labeling, and the line between compression/cleanup and manipulation.

The Debate: Compression Artifacts vs. Intentional AI A contentious technical debate emerged regarding whether these changes are truly "AI retouching" or simply aggressive compression artifacts. User Aurornis was a vocal skeptic, arguing that "swimming blocks," smoothing, and motion artifacts are standard consequences of low bitrates, and criticized non-technical influencers for interpreting these flaws as intentional beauty filters without raw file evidence.

However, mxbnd and others pushed back, arguing that the technical "why" is less important than the result. They contended that if the processing—whether via upscaling, de-noising, or compression—results in "waxy" skin, enlarged eyes, or altered features, it functionally acts as a non-consensual filter. whstl noted that creators like Rick Beato are audio/video experts capable of distinguishing between standard codec artifacts and new, unnatural processing.

Frustrations with "Auto-Everything" The conversation broadened to other instances of platforms overriding user and creator intent with AI.

• Auto-Dubbing: Users expressed significant annoyance with YouTube’s auto-translation features. TRiG_Ireland and sfx described the frustration of clicking a video with an English title only to hear a jagged AI dub, with no easy way to access the original audio or subtitles.
• Bilingual Issues: Several commenters noted that these automated features break the experience for bilingual users, as algorithms often force content into a region’s default language rather than the user's preferred or original language.

Terms of Service and Ownership A smaller segment of the discussion focused on the legal reality. rctrdv and p pointed out that while creators feel violated, platform Terms of Service likely grant YouTube broad rights to modify files for "optimization" or distribution. The consensus was that this represents a "rude awakening" for creators regarding who actually owns the presentation of their work once it is uploaded to a centralized platform.

Advent of Code 2025: The AI Edition – By Peter Norvig

Submission URL | 42 points | by vismit2000 | 12 comments

Peter Norvig’s “pytudes” is a beloved, long-running collection of short, well-explained Python notebooks and scripts that tackle algorithms, AI/search, word games, probability, and programming puzzles. It’s equal parts study guide and showcase of clean problem-solving, with worked examples like a spelling corrector, Sudoku and crossword solvers, search/CSP techniques, and Advent of Code solutions. Great for self-learners and interview prep alike, the repo emphasizes clear thinking, readable code, and literate, testable notebooks.

Discussion Summary:

• LLMs & Advent of Code: Much of the conversation revolves around Norvig’s experiments using LLMs to solve Advent of Code (AoC) challenges. Users debated the ethics of this practice; the consensus suggests that while using AI for learning or personal experimentation is fascinating, submitting AI-generated solutions to the AoC leaderboards violates the spirit of the competition. One user joked that using LLMs might get one's "programmer card revoked," though others appreciated the comparison between human and LLM problem-solving strategies.
• AI Fatigue vs. Utility: A skeptical thread emerged questioning the value of these experiments, describing LLMs as "calculators with a probability of failure" and expressing exhaustion with constant AI "hype."
• The Rebuttal: Other users defended the post, pointing out that Peter Norvig is a seminal figure in AI history whose experiments are inherently valuable. Commenters argued that sharing positive experiences with tools isn't necessarily "hype," and pointed out the irony of complaining about AI noise while simultaneously adding to the noise with cynical takes.
• Technical Details: Outside the meta-discussion, there were brief technical exchanges regarding specific code logic (involving half_digits variations) and mentions of Google's Gemini models in the context of coding assistance.

AI Submissions for Fri Dec 05 2025

Gemini 3 Pro: the frontier of vision AI

Submission URL | 506 points | by xnx | 265 comments

Gemini 3 Pro: Google’s new multimodal model pushes hard on visual and spatial reasoning

What’s new

• Google DeepMind claims state-of-the-art results across vision-heavy tasks: document, spatial, screen, and video understanding, topping benchmarks like MMMU Pro and Video MMMU.
• Big focus on “derendering”: turning images of messy, real-world documents into structured code (HTML/LaTeX/Markdown). Demos include reconstructing 18th‑century handwritten tables, equations from photos, and Florence Nightingale’s polar chart into an interactive graphic.
• Document reasoning: The model navigates long reports, cross-references figures/tables, and ties numbers to causal text. It reportedly beats the human baseline on the CharXiv Reasoning benchmark (80.5%), with an example analyzing Gini index changes and policy impacts in a 62-page Census report.
• Spatial understanding: Outputs pixel-precise coordinates to “point” in images; supports open‑vocabulary references (e.g., “point to the screw”) for robotics/AR planning and manipulation.
• Screen understanding: Parses desktop/mobile UIs with high-precision clicking—pitched for reliable “computer use” agents, QA, onboarding, and UX analytics.
• Video: Higher frame-rate comprehension (e.g., 10 FPS) to catch fast actions like golf swings and weight shifts.

Why it matters

• If the claims hold, this closes gaps between perception and reasoning across messy real-world inputs—key for automation in back-office document workflows, UI agents, robotics, and sports/industry video analysis.

Caveats

• These are vendor-reported benchmarks and demos; independent evaluations and real-world reliability (latency, cost, privacy) will be crucial.
• Developers can try it via Google AI Studio and docs, but details on pricing, rate limits, and on-device/enterprise deployment weren’t included here.

Here is a summary of the discussion:

The "Five-Legged Dog" Stress Test The majority of the discussion focuses on a specific stress test: showing the model a picture of a dog with five legs. Users report that despite the model’s claimed visual precision, it struggles to override its training priors (that dogs have four legs).

• Cognitive Dissonance: When asked to count legs, Gemini and other models often hallucinate explanations for the fifth limb (e.g., calling it a tail, an optical illusion, or claiming the dog is an amputee) to fit the "4-leg" model.
• Implicit vs. Explicit: Use vndrb noted that while the model fails at counting the legs, it succeeds at editing tasks. When asked to "place sneakers on the legs," the model correctly placed five sneakers, suggesting the visual encoder sees the data, but the reasoning layer suppresses it.
• Generative Struggles: Users noted similar failures when asking models to generate out-of-distribution concepts, such as a "13-hour clock." The models consistently revert to standard 12-hour faces or hallucinate workarounds (like adding a plaque that says "13") rather than altering the fundamental structure.

The Role of RLHF Commenters speculate that Reinforcement Learning from Human Feedback (RLHF) is the culprit. The consensus is that models are heavily penalized during training for deviating from "normal" reality. Consequently, the models prioritize statistical probability (dogs usually have four legs) over the immediate visual evidence, leading to "stubborn" behavior where the model refuses to acknowledge anomalies.

NeurIPS 2025 Best Paper Awards

Submission URL | 170 points | by ivansavz | 28 comments

NeurIPS 2025 named seven Best Paper Award winners (four Best Papers, including one from the Datasets & Benchmarks track, and three runner-ups), spanning diffusion theory, self-supervised RL, LLM attention, reasoning, online learning, neural scaling laws, and benchmarking for model diversity. Committees were drawn from across the program, dataset/benchmark tracks, and approved by general and accessibility chairs.

Two standouts highlighted in the announcement:

• Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)

  • Releases Infinity-Chat, a large open-ended benchmark of 26K real-world prompts plus 31,250 human annotations (25 raters per example) and a first comprehensive taxonomy of open-ended LM tasks (6 categories, 17 subcategories).
  • Empirically shows an “Artificial Hivemind” effect: strong intra-model repetition and inter-model homogeneity on open-ended generation.
  • Finds miscalibration between reward models/LM judges and diverse human preferences, underscoring the tension between alignment and pluralism and the long-term risk of creativity homogenization.

• Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

  • Systematically studies gating in softmax attention across 30 model variants, including 15B MoE and 1.7B dense models trained on 3.5T tokens.
  • A simple tweak—adding a head-specific sigmoid gate after scaled dot-product attention—consistently boosts performance, improves training stability, tolerates larger learning rates, and scales better.
  • Points to benefits from injecting non-linearity in the attention path (and addresses attention sink issues).

Why it matters

• Evaluation is moving beyond narrow benchmarks to plural, open-ended human preferences—raising flags about model homogenization and the cost of over-alignment.
• Small architectural changes can still unlock meaningful gains at trillion-token scale.
• The award slate balances societal impact with core theory and systems advances—signaling where ML research energy is heading.

Here is a summary of the top stories and the accompanying discussion:

NeurIPS 2025 Announces Best Paper Awards The NeurIPS 2025 committee has selected seven winners for the Best Paper Awards, highlighting a shift in machine learning research toward analyzing model homogeneity and refining architectural fundamentals. Two papers were specifically highlighted in the announcement:

1. "Artificial Hivemind" releases Infinity-Chat, a benchmark of 26k prompts, revealing that LLMs exhibit strong "hivemind" behavior—repetitive internal outputs and high homogeneity across different models—suggesting a long-term risk of creativity loss due to misalignment between reward models and diverse human preferences.
2. "Gated Attention for Large Language Models" introduces a simple architectural tweak—adding a sigmoid gate to softmax attention—which improves training stability and performance at the trillion-token scale. Overall, the awards signal a move beyond narrow benchmarks toward open-ended evaluation and demonstrate that small structural changes can still yield significant gains.

Summary of Hacker News Discussion The discussion thread focuses on the validity of benchmarking metrics, the theoretical underpinnings of reasoning, and the changing demographics of ML researchers:

• RL and Reasoning Capacity: A significant debate centers on whether Reinforcement Learning (RL) truly improves a model's reasoning capabilities or merely limits its creativity. Users discuss the "Does Reinforcement Learning Incentivize Reasoning...?" paper, arguing over the validity of "pass@k" metrics. Skeptics argue that RL simply "sharpens" the probability distribution toward answers already present in the base model (which acts as a broader, more creative generator), while proponents argue that pass@k is a valid proxy for skill, distinguishing actual correctness from the theoretical possibilities of a "random number generator."
• The "Hivemind" Effect: Users experimented with the "Artificial Hivemind" paper's findings by prompting models (like Gemini) to write metaphors about time. Commenters noted that while models produced varying imagery (cliffs, hammers), the underlying semantic themes usually reverted to the dominant "river/flow" cluster, validating the paper's claims about model homogeneity.
• Physicists in ML: Commenters noticed several physicists among the award winners. This sparked a conversation about the high transferability of physics skills (linear algebra, eigenvectors, SVD) to Machine Learning, with some suggesting physicists are better equipped for the math-heavy interaction of ML than standard software engineers.
• Consumption and "Slop": In a discussion about how best to digest these papers (reading vs. video), the tool NotebookLM was mentioned. Opinions were split: some view AI-generated audio summaries as "environmental pollution" cluttering search results, while others argued they are actually an improvement over the low-quality "slop" videos produced by humans.
• Architecture & Superposition: There is speculation regarding "superposition" in neural networks—specifically how differentiable networks struggle to "commit" to a single concept (e.g., green vs. purple) without the "forcing function" of discretizing tokens. Other architectural papers, such as TITANS and work by SakanaAI, were recommended as complementary reading.

Jony Ive's OpenAI Device Barred From Using 'io' Name

Submission URL | 83 points | by thm | 59 comments

Jony Ive/OpenAI barred from using “io” hardware brand after Ninth Circuit upholds TRO

• A U.S. appeals court affirmed a temporary restraining order blocking OpenAI, Jony Ive, Sam Altman, and IO Products, Inc. from using “io” to market hardware deemed similar to AI-audio startup iyO’s planned device (source: Bloomberg Law via MacRumors).
• The court found a likelihood of confusion between “IO” and “iyO” and flagged “reverse confusion” risk given OpenAI’s scale, citing potential irreparable harm to iyO’s brand and fundraising.
• Backstory: Ive and Altman picked “io” in mid‑2023. In early 2025, iyO CEO Jason Rugolo sought funding from Altman for a human‑computer interface project; Altman declined, saying he was already working on something competitive. OpenAI argued its first device wouldn’t be a wearable and that Rugolo voluntarily shared details while suggesting a $200M acquisition.
• Scope: The order doesn’t ban all “io” uses—only for hardware similar to iyO’s planned AI-audio computer. OpenAI removed “io” branding shortly after the TRO.
• What’s next: The case returns to district court for a preliminary injunction hearing in April 2026; broader litigation could run into 2027–2028. OpenAI’s first hardware device is still expected next year, likely under a different name.

Why it matters for HN:

• Highlights the “reverse confusion” doctrine—when a big brand risks swamping a smaller mark.
• Naming due diligence for hardware/AI products just got a high-profile cautionary tale.
• Signals branding and launch risks for OpenAI’s Ive-designed device even as the product timeline advances.

Based on the discussion, Hacker News users reacted with a mix of branding critique, mockery of the founders' public image, and speculation regarding the utility of the hardware itself.

Branding and Alternatives The court order barring "io" sparked immediate humor and alternative suggestions. Several users jokingly proposed "Oi" (referencing British slang and Jason Statham movies), though others noted "Oi" is already a major telecom brand in Brazil. Others referenced "JOI" (from Blade Runner) or the bygone "Yo" app. On a serious note, commenters questioned the strategy behind the original name, arguing that "io" is uncreative, difficult to search for in hardware repositories, and squanders the immense brand equity of "ChatGPT," which one user felt should have been the leading name for the device.

Critique of the Ive/Altman "Vibe" A thread developed specifically roasting the press photo of Sam Altman and Jony Ive. Users described the aesthetic as "creepy," comparing it variously to a "bad early 90s TV movie," a "cropped Giorgio Armani perfume ad," or a "pregnancy announcement," with some viewing the project as a "narcissistic dance-off."

Speculation on the Hardware Discussion shifted to what the device actually does, with significant skepticism:

• Form Factor: Guesses ranged from a "Humane Pin v2" to smart glasses, a set-top TV box, or a dedicated smart speaker.
• Utility: Some users expressed desire for a dedicated "ChatGPT box" to replace existing smart speakers (Alexa/Google Home), which many felt have become "detuned" or increasingly useless.
• Necessity: Users theorized that OpenAI is forced to build hardware because Apple will never grant a third-party app the "always-on," deep-system access required for a true AI assistant on the iPhone.
• Viability: Cynicism remained high, with comparisons to other recent AI hardware flops like the Rabbit R1 or Humane Pin, with one user calling it likely just a "chatbot box."

The Plaintiff (iyO) A few users investigated the plaintiff, iyO, noting that their planned products resemble "audio computing" headphones or cameras, though one user complained that the startup's website was incredibly slow to load.

Wall Street races to protect itself from AI bubble

Submission URL | 70 points | by zerosizedweasle | 83 comments

Wall Street races to protect itself from the AI bubble it’s funding

• Banks are underwriting record borrowing to build AI infrastructure while simultaneously hedging against a potential bust. Global bond issuance has topped $6.46T in 2025 as hyperscalers and utilities gear up to spend at least $5T on data centers, per JPMorgan.
• Anxiety is visible in credit markets: the cost to insure Oracle’s debt has climbed to highs not seen since the Global Financial Crisis, and hedging activity has exploded. Oracle CDS trading hit about $8B over nine weeks through Nov 28 vs ~$350M a year earlier.
• Lenders are heavily exposed via massive construction loans (e.g., $38B and $18B packages tied to new data centers in Texas, Wisconsin, and New Mexico) and are offloading risk with credit derivatives and portfolio deals.
• CDS spreads have jumped across big tech. Five-year protection on $10M of Microsoft debt runs 34 bps ($34k/yr) vs ~20 bps in mid-October; Johnson & Johnson, the only other AAA in the U.S., is ~19 bps. Saba Capital says MSFT protection looks rich and is selling it; they see similar dislocations in Oracle, Meta, and Alphabet.
• Operational risk is in the mix: a major outage that halted CME Group trading prompted Goldman Sachs to pause a $1.3B mortgage bond sale for data center operator CyrusOne—highlighting how repeated breakdowns can drive customer churn.
• Morgan Stanley has explored “significant risk transfer” deals—using credit-linked notes and similar structures to insure 5–15% of designated loan portfolios—and private credit firms like Ares are positioning to absorb that risk.
• Why it matters: The AI buildout may be the largest tech borrowing spree ever, but banks are laying off downside to derivatives buyers and private credit. If returns lag or outages mount, losses won’t stay on bank balance sheets; if not, hedgers and protection sellers could win. As Steven Grey cautions, great tech doesn’t automatically equal profits.

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

Fears of a Bailout and "Privatized Gains, Socialized Losses" The most prominent reaction to the article is cynicism regarding who will ultimately pay for a potential AI bust.

• Users suggest that while banks are currently hedging, the US government (and by extension, the taxpayer) will "step in" to bail out AI corporations and Wall Street if the bubble bursts.
• One commenter satiricaly proposes a government plan involving borrowing trillions to give children equity quotas, highlighting the absurdity of current national debt levels and the feeling that the financial system is playing "God" with economics.
• One brief comment summed up the sentiment with the phrase "Make America Bankrupt."

The "AI Arms Race" Justification A counter-argument emerged claiming that the massive spending and borrowing are necessary matters of national defense.

• Several users argue the US cannot afford to "sleep" while China advances. The consensus among this group is that the AI buildout is a geopolitical necessity to prevent China from becoming the sole dominant power.
• Parallels were drawn to Cold War logic ("Mr. President, we cannot allow a mineshaft gap"), suggesting that even if the economics are a bubble, the strategic imperative overrides financial caution.

Debate on China’s Stability and Data The mention of China sparked a sub-thread about the reliability of Chinese economic data and their motivations for pursuing AI.

• One user argued that China is betting on AI and robotics to solve its looming demographic collapse and leverage its future despite a shrinking workforce.
• Others disputed the reliability of information regarding China, with some asking for a "single source of truth." There was a debate over whether Chinese official statistics (Five Year Plans, National Bureau of Statistics) are reliable or comparable to manipulated Soviet-era propaganda.

Macroeconomic Theory and Money Printing A significant portion of the discussion devolved into a technical debate about the nature of money and debt.

• Users argued over the definition of "printing money" versus "issuing debt."
• Some contended that debt functions as savings for others (e.g., China buying US Treasuries) and is distinct from printing money, while others argued that fractional reserve banking essentially allows banks to create money out of thin air, expanding the money supply and fueling inflation.
• This thread reflected broader anxiety about the long-term sustainability of US fiscal policy, referencing recent increases in credit default swaps and huge deficit spending.