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AI Submissions for Tue Apr 08 2025

Obituary for Cyc

Submission URL | 408 points | by todsacerdoti | 216 comments

In the realm of artificial intelligence, few names evoke as much intrigue as Douglas Lenat's, particularly due to his monumental Cyc project. Begun in 1985, Cyc aimed to crack the nut of artificial general intelligence through an ambitious strategy: scaling symbolic logic by encoding vast amounts of common sense knowledge. Lenat believed that for AI to truly learn and reason like humans, it needed a massive repository of world knowledge to draw from—a theory steeped in his experiences with past projects like the Automated Mathematician and EURISKO.

Despite Lenat's unwavering dedication and the project's staggering investment—culminating in 30 million assertions at the cost of $200 million and 2,000 person-years—the breakthroughs promised never materialized. The Cyc system, conceived as a groundbreaking "knowledge pump," never achieved the self-sustaining knowledge expansion and learning Lenat envisioned. Instead, its practical applications remained rooted in established AI methodologies, akin to those employed by tech giants like Oracle and IBM, offering no discernible competitive edge.

The project's long-term sustainability can partly be attributed to substantial funding from military and commercial sectors. However, this financial footing didn’t translate into revolutionary academic contributions or public success stories. As academia largely shunned Cyc for its inaccessibility and lack of performance on public benchmarks, the project became an insular endeavor, closed off from the outside innovation swirling around in AI research.

Despite the ultimate lack of success in achieving its grand ambitions, the Cyc project stands as a fascinating chapter in AI history—a testament to the challenges of scaling symbolic logic for general intelligence. The story of Cyc, and the "secret history" behind it, underline the complexities and limits of symbolic approaches in AI, raising questions—and lessons—for the field's ongoing evolution. Today, with much of Cyc's archival materials now available for public scrutiny on platforms like GitHub, the legacy of Douglas Lenat's quest sheds light on the perpetual struggle to teach machines to understand the world as deeply as humans do.

The Hacker News discussion revolves around the legacy of Douglas Lenat’s Cyc project and its contrast with modern AI approaches like Large Language Models (LLMs). Here’s a concise summary of key points:

Cyc’s Ambitions vs. Reality

• Cyc’s Goal: Encode "common sense" via symbolic logic (e.g., understanding that a person can’t shave with an unplugged electric razor).
• Critique: Despite decades of effort and millions of hand-coded assertions, Cyc struggled with scalability, brittleness, and practical relevance. Participants noted its failure to achieve self-sustaining learning or outperform traditional rule-based systems (e.g., Prolog) or newer data-driven methods.

Symbolic AI vs. LLMs

• Symbolic AI (Cyc/GOFAI): Relied on explicit rules and logic but was limited by manual curation and inability to handle real-world ambiguity. Examples like SAT solvers and planners were praised for structured problems but criticized for lacking flexibility.
• LLMs: Praised for scaling via vast data and "fuzzy" contextual understanding. While not perfect, LLMs excel at generating plausible outputs by recognizing patterns, even if they lack true reasoning. Critics argued LLMs still struggle with logical rigor (e.g., transitive relations).

Hybrid Approaches

• Bridging the Gap: Some suggested combining LLMs (for natural language understanding) with symbolic systems (for structured reasoning). For example, using LLMs to translate natural language into Prolog/Python code or structured formats (JSON) for traditional computation.
• Use Cases: LLMs could handle ambiguous tasks (e.g., parsing nutrition labels), while symbolic systems manage precise logic (e.g., scheduling, math).

Legacy and Lessons

• Cyc’s Legacy: A bold experiment highlighting the challenges of manual knowledge engineering. Its closed, insular development contrasted with today’s open, collaborative AI research.
• Modern Parallels: Debates echoed around whether LLMs are merely "stochastic parrots" or stepping stones toward AGI. Participants acknowledged that neither symbolic nor statistical methods alone suffice for general intelligence.

Final Takeaway

The discussion underscores a shift from rigid symbolic systems (Cyc) to flexible, data-driven models (LLMs), while acknowledging that hybrid approaches—leveraging the strengths of both paradigms—might be the future of AI. Cyc remains a cautionary tale about scalability and the limits of human-curated knowledge, even as LLMs redefine what’s possible.

Solving a “Layton Puzzle” with Prolog

Submission URL | 101 points | by Tomte | 27 comments

Have you ever wanted to solve a brain-tickling puzzle using logic programming? Here's a delightful tale of solving a "Layton Puzzle" using Prolog—a classic logic programming language. Imagine a test with 10 questions, each with true/false options, and you'd like to determine the fourth student's score based on the scores of the first three students with known answers. Pablo Meier once tackled a similar challenge, using Prolog to crack the code, but this tale goes a step further with an elegant solution in fewer lines of code.

Firstly, here's the setup: You know how three students scored and their answers, and need to predict the elusive score of the fourth student. With a quick dive into Prolog, the programmer begins by importing essential modules for handling inequalities and setting up constraints. Using Prolog’s pattern matching prowess, we create a recursive function to calculate a score by comparing answers to a key, cleverly bypassing more procedural if-else statements traditional programming uses.

The magic of Prolog shines here with its bidirectionality; you can use the program both to verify a known score and to deduce an unknown one. Through predicates and mappings, Prolog also generates potential answer keys, further showcasing the elegant and multipurpose nature of the language.

When the program runs, it finds four possible keys all producing the same score for the mystery student, revealing that the puzzle isn't broken but intriguingly self-consistent. This logic solution not only solves the problem but does so with remarkable efficiency—15 lines to accomplish what took 80 lines in another version. It's puzzles like these and solutions like this that show the enduring magic of logic programming.

So if you find yourself pondering logic puzzles or practical applications with Prolog, keep in mind the potential simplicity behind seemingly complex challenges. Plus, who doesn't want a chance to show up a fellow programmer with fewer lines of smarter code?

The Hacker News discussion revolves around solving a Professor Layton logic puzzle using Prolog and alternative methods, with key points summarized below:

Technical Solutions

1. Prolog & Constraint Programming

   • Users shared code snippets using Prolog’s clpfd library to model the puzzle, emphasizing brevity (e.g., 15-line solution). The approach leverages constraints to find valid answer keys, revealing Colin’s score as 60 across four possible configurations.
   • Debates arose about Prolog’s practicality, comparing implementations (SWI-Prolog, SICStus) and praising its bidirectional reasoning for logic puzzles.

2. Z3 in Python

   • A user demonstrated a concise 7-line solution using Z3, a theorem prover, to model the problem declaratively. Others highlighted its simplicity compared to Prolog (example blog post).

Human-Logic Approaches

• A manual, heuristic-driven breakdown analyzed overlaps in student answers to deduce Colin’s score. For instance, comparing Mary’s and Dan’s incorrect answers narrowed down possible correct keys, concluding Colin scored 60 by leveraging shared patterns.

Nostalgia & Puzzle Design

• Fans reminisced about the Professor Layton game series (specifically Diabolical Box), praising its puzzle design. Some linked the challenge to classic logic puzzles like the Zebra Puzzle, often tackled with Prolog.

Broader Discussions

• Prolog’s Relevance: A tangent debated Prolog’s decline in popularity despite its strengths in constraint-solving, referencing a 2023 thread on its historical context.
• Educational Value: Users emphasized puzzles like these for teaching constraint programming and reducing trial-and-error through structured logic.

Key Takeaways

• The puzzle highlights the elegance of logic programming (Prolog) and modern solvers (Z3).
• While Prolog’s syntax and ecosystem quirks drew criticism, its suitability for combinatorial problems remains unmatched.
• Community appreciation for both computational and manual problem-solving underscored the puzzle’s design and educational appeal.

Neural Graffiti – Liquid Memory Layer for LLMs

Submission URL | 103 points | by vessenes | 25 comments

In today's tech buzz, a fascinating project called "Neural Graffiti" is making waves on Hacker News. Developed by the user "babycommando," this innovative approach takes the artistry of graffiti and marries it with the neuroplasticity of the brain, applied to large language models (LLMs). This experimental layer, known as the "Spray Layer," is positioned within the final stages of transformer model inference, requiring no fine-tuning or retraining.

Inspired by liquid neural networks, Neural Graffiti allows for real-time behavior modulation of pre-trained models. By injecting memory traces directly into vector embeddings, it subtly changes the "thinking" of a model over time, encouraging the model to lean into certain concepts more naturally as it interacts. This method is akin to a "soft whisper" influencing the model's perception and internal state.

What makes this technique intriguing is its potential for developing AI with a more active personality and enhanced curiosity, akin to a digital persona finding itself. However, the developers note that while this may not be suited for commercial deployments, it's a creative exploration in AI self-awareness and memory.

The project is open-source, hosted on GitHub, and the demo is available on Google Colab, inviting AI enthusiasts and researchers to explore its potential further. As AI continues to evolve, Neural Graffiti is a step towards creating AI entities with unique character traits and rich internal mental landscapes.

The Hacker News discussion on the Neural Graffiti project reflects a mix of technical curiosity, skepticism, and broader debates about AI behavior. Key points from the comments include:

1. Technical Scrutiny: Users debated the mechanics of the "Spray Layer," comparing it to existing methods like LoRA (Low-Rank Adaptation) and EMA (Exponential Moving Average) vectors. Some questioned whether the approach genuinely introduces novel memory retention or merely resembles established techniques, with discussions around random weight initialization and reservoir computing.

2. Skepticism About Efficacy: Several users tested the demo and expressed doubts about its ability to retain context or influence model behavior meaningfully. One user noted the model failed to remember concepts even after repeated prompts, sparking debates about whether observed changes were due to actual memory or random artifacts.

3. Comparisons to Commercial AI: The conversation veered into critiques of mainstream models like ChatGPT and Claude, highlighting issues like "sycophancy" (overly agreeable responses) and inconsistent behavior. Users contrasted OpenAI’s approach with alternatives, suggesting Claude handles conversations more naturally.

4. Criticism of Novelty: Some dismissed Neural Graffiti as a "buzzword-laden" project, questioning its innovation compared to frequent reinventions in the industry. Others acknowledged the creative naming (e.g., "liquid memory layer") but remained unconvinced of its technical substance.

5. Safety and Ethics: Concerns were raised about AI models inadvertently producing harmful outputs, with mentions of challenges in moderating responses related to controversial topics or figures.

Overall, the discussion highlighted both intrigue about Neural Graffiti’s experimental goals and skepticism about its execution, while broader themes around AI behavior and industry trends dominated tangential threads.

Vishap Oberon Compiler

Submission URL | 18 points | by sevoves | 7 comments

The Vishap Oberon Compiler (voc) has proudly made its presence felt on Hacker News! This open-source project brings the elegance of the Oberon-2 language to modern operating systems, including Linux, BSD, Android, macOS, and Windows, by leveraging a C backend with popular compilers like gcc, clang, and tcc.

Complete with libraries borrowed from the Ulm, oo2c, and Ofront Oberon compilers, Vishap’s implementation adheres to the Oakwood Guidelines, ensuring robust functionality for all users. The documentation is thorough, offering a step-by-step installation process and examples to help users get started with compiling their first Oberon programs.

Whether you're a Linux user, BSD aficionado, or even a Windows enthusiast, the Vishap Oberon Compiler has you covered with its comprehensive installation instructions. The community around this project has contributed code and expertise, elevating it to a polished and capable tool for anyone interested in exploring the Oberon-2 language.

With its open-source nature under the GPL-3.0 license, this project invites developers to tinker, improve, and potentially contribute back to the ecosystem. If you want a taste of Oberon's simplicity and power, Vishap's Compiler is right up your alley. Visit the project repository to start compiling, and perhaps contribute your own code to this active community!

Summary of Discussion:

The discussion around the Vishap Oberon Compiler highlights efforts to modernize Oberon-2 and integrate it into contemporary systems while acknowledging historical challenges. Key points include:

1. UNIX Legacy & Vision: A user alludes to the difficulty of transcending UNIX-based paradigms, metaphorically framing it as an unfulfilled task for modern systems. This sets a backdrop for Oberon’s role in exploring alternative approaches.

2. Compiler Integration & Standards:

   • The compiler’s adherence to the Oakwood Guidelines ensures system-independent functionality, with libraries like NAppGUI and SDL bindings enabling cross-platform graphical interfaces.
   • Discussions mention native compilation aspirations and projects like Dusk OS21, which aim to embed Oberon into graphical systems using SDL.

3. Technical Challenges:

   • Users note transpiling efforts (e.g., leveraging C backends) and compatibility hurdles, such as integrating OS-specific functions (e.g., file operations) via libraries like libc and HTTP packages.
   • Existing codebases (e.g., Vishap’s compiler and VIPack) are cited for enabling practical workflows, though scattered compatibility layers and code portability remain challenges.

4. Community Contributions:

   • Appreciation is shown for alternative Oberon implementations (e.g., Rochus Keller’s OberonSystem3) and tools that blend Oberon with modern syntax or libraries.
   • Projects like http libraries and package managers demonstrate ongoing efforts to expand Oberon’s ecosystem.

The conversation underscores a blend of nostalgia for Oberon’s design principles and pragmatic steps to evolve it—using modern tooling, community-driven libraries, and cross-platform frameworks—to stay relevant in today’s programming landscape.

Meta got caught gaming AI benchmarks

Submission URL | 328 points | by pseudolus | 157 comments

Meta recently found itself in hot water after it was revealed that the tech giant may have gamed AI benchmarks to make its new Llama 4 models appear more competitive. Over the weekend, Meta released two new models, Scout and Maverick, asserting that Maverick outperformed OpenAI's GPT-4o and others on LMArena, a popular comparison site for AI outputs. However, eagle-eyed AI researchers discovered a crucial bit of information buried in Meta’s documentation: the version of Maverick tested was an "experimental chat version" specifically designed to excel in conversational tasks.

This revelation prompted LMArena to clarify its policies, emphasizing the importance of fair and reproducible evaluations. Meta's spokesperson responded, highlighting their extensive experimentation with custom versions of their AI models. While not violating direct LMArena rules, the incident raises serious concerns about the integrity of AI benchmarks. Developers often rely on these scores to make informed decisions about which AI models to use, but if companies like Meta submit souped-up versions that aren't publicly available, these benchmarks lose their reliability as tools for assessing true performance.

The timing of the release further fueled speculation, as it dropped on a Saturday—a rarity in the tech world. Meta CEO Mark Zuckerberg simply noted, "That’s when it was ready." The mixed messages and questionable tactics have left many in the AI community scratching their heads, pondering whether benchmarks are becoming more about gaming the system rather than providing genuine insights into AI capabilities. This saga underscores the increasing competitiveness in AI development as companies vie for leadership and recognition.

The Hacker News discussion revolves around skepticism toward Meta’s AI benchmarking practices and connects it to broader criticisms of the company’s internal culture and management strategies. Key themes include:

1. Benchmark Gaming Concerns:
   Users speculate that Meta’s claim of outperforming GPT-4o with its experimental "Maverick" model may reflect a focus on optimizing for benchmarks rather than genuine performance. This ties to fears that benchmarks are becoming marketing tools instead of reliable evaluations.

2. Cultural Critiques:

   • "Move Fast and Break Things" Legacy: Commenters argue Meta’s historical emphasis on speed over quality fosters rushed, half-baked releases. This leads to technical debt and undermines long-term product reliability.
   • Performance-Driven Pressures: Employees are incentivized to prioritize metrics (e.g., launching features quickly) over quality to meet review cycles, exacerbating issues like technical debt and unstable AI models.

3. Management and Incentive Structures:

   • Layoffs and high turnover are critiqued for reducing institutional knowledge, leaving fewer skilled workers to manage complex projects.
   • The Hawthorne Effect and McNamara Fallacy are cited: Management’s focus on short-term metrics (e.g., quarterly results) creates temporary performance boosts but neglects sustainable progress, leading to employee burnout and rushed releases.

4. Data Quality Questions:
   Skepticism arises about whether Meta’s AI improvements stem from better data or are merely tactics to game benchmarks. Users question if the company’s internal data management can support claims of superior model performance.

5. Broader Tech Industry Parallels:
   Comparisons to Netflix’s high-pressure culture highlight systemic issues in tech, where aggressive performance incentives prioritize rapid delivery over innovation and employee well-being.

Conclusion: The discussion reflects distrust in Meta’s benchmarking claims, linking them to a culture that prioritizes short-term wins and metrics over transparency and quality. Critics argue this undermines both the reliability of AI evaluations and the sustainability of Meta’s advancements.

Cogito Preview: IDA as a path to general superintelligence

Submission URL | 38 points | by parlam | 3 comments

In an exciting leap towards achieving general superintelligence, Cogito has unveiled its latest suite of large language models (LLMs) in various sizes from 3 billion to 70 billion parameters. These models, launched under an open license, set a new benchmark by outperforming all existing open-source models of equivalent sizes, even beating advanced models like the recently released Llama 4 109B MoE.

The magic behind these superior LLMs is the innovative Iterated Distillation and Amplification (IDA) training strategy. IDA offers a pathway to break free from the inherent limitations set by human overseers, ensuring consistent self-improvement without their bounds. This method enhances the model's reasoning by iteratively amplifying intelligence through computational power before distilling these capabilities back into the model, creating a powerful feedback loop of growth.

What this means is these models can think like traditional LLMs, and also engage in self-reflective reasoning, making them robust tools for complex problem-solving and agentic tasks. As a testament to their efficiency, the Cogito team developed these models swiftly in just over two months, indicating the scalability and time effectiveness of their method.

The excitement doesn’t stop here—larger models, including the ambitious 671 billion parameter model, are on the horizon. For developers and researchers eagerly awaiting to tinker with these cutting-edge models, they're readily accessible on platforms like Huggingface or through APIs on Fireworks AI and Together AI.

The success of Cogito’s models in industry-standard benchmarks not only validates IDA's potential but paves a structured path to transcend today's AI intelligence limits, nudging closer to the dawn of general superintelligence. As this journey unfolds, these models promise to adapt well beyond dry benchmarks, aiming to deliver powerful real-world solutions tailored to user needs.

Summary of Discussion:

1. Ethical Concerns (rndphs):
   Commenters express apprehension about developing AGI (Artificial General Intelligence) targeting superintelligence, citing ethical risks. Superintelligent AI, they argue, could displace humanity—a widely acknowledged danger among philosophers and researchers.

2. Technical Explanation of IDA (Reubend):
   The Iterated Distillation and Amplification (IDA) method is outlined:

   • Amplification: Leveraging computational power to create higher intelligence.
   • Distillation: Encoding these advanced capabilities into model parameters.
     Reubend highlights IDA’s potential to enable self-improvement beyond human-generated training data, possibly leading to superintelligence. They praise the open-source availability (Hugging Face, Ollama, etc.) as a significant step forward.

3. Skepticism and Hype-Checking (bbr):
   Skeptics question the submission’s claims, suspecting overhyped marketing or gatekeeping. They demand concrete benchmarks and evidence over promotional screenshots. Concerns are raised about IDA’s unpredictability, including the risk of an uncontrollable “intelligence explosion” surpassing human oversight.

4. Mixed Reactions:
   While some applaud the technical ambition and open-access approach, others urge caution, emphasizing the need for transparency and validation. The discussion reflects both excitement for progress and wariness of unproven claims or existential risks.

Key Themes:

• Ethical dilemmas of superintelligent AI.
• Technical optimism vs. skepticism about IDA’s feasibility.
• Calls for empirical proof over marketing.
• Open-source accessibility as a positive step.

Tom and Jerry One-Minute Video Generation with Test-Time Training

Submission URL | 79 points | by walterbell | 16 comments

Introducing a breakthrough in video generation technology, researchers have innovatively incorporated Test-Time Training (TTT) layers into pre-trained Transformers, allowing them to craft more coherent and expressive one-minute videos from text prompts. This development marks a significant leap from traditional self-attention layers, which struggle with long contexts, and alternatives like Mamba layers that falter with intricate stories. Utilizing a dataset drawn from classic Tom and Jerry cartoons, the experiment revealed that TTT layers vastly improved video coherence and storytelling, outperforming other methods such as Mamba 2, Gated DeltaNet, and sliding-window attention by 34 Elo points in human evaluations.

While promising, the generated content still grapples with certain artifacts, attributed to the limited capabilities of the current 5B model. Characters and scenes occasionally exhibit inconsistencies, such as color variations and unrealistic motion physics. Despite these challenges, the approach showcases strong potential for generating longer and more complex video narratives in the future.

Credit goes to a collaboration among researchers from institutions including NVIDIA, Stanford, UCSD, UC Berkeley, and UT Austin, with support from Hyperbolic Labs. As a proof of concept, this advancement lays the groundwork for future developments in video generation, pushing boundaries towards refined storytelling via AI.

Summary of Discussion:

1. Technical Feasibility and Cost:

   • Users debated the computational resources required (50+ hours on 256 H100 GPUs) to train the model. While some found this "impressively low" for research-grade work, others questioned the expense, especially for generating short video clips. Skyylr noted skepticism about whether the cost justifies the output quality, while brgrkng contrasted these costs with the resource constraints faced by human creators in traditional industries.

2. Impact on Creativity and Jobs:

   • A heated debate emerged about AI’s role in creativity. Critics like brgrkng argued that AI-generated content risks displacing human creativity, reducing art to "copy-pasting" and undermining industries like film (e.g., LOTR, Star Wars). Others, however, countered that AI could democratize content creation, enabling new forms of expression despite concerns about homogenization.

3. Quality and Progress:

   • While kfrwsmn acknowledged flaws in current outputs (e.g., artifacts, inconsistencies), they praised the rapid progress compared to older models. Skeptics like nmrsp questioned whether video-generation advancements truly represent meaningful progress, dismissing hype around "unlimited customizable content" as overblown.

4. Ethical and Cultural Implications:

   • quantumHazer raised concerns about personalized content creating "filter bubbles," limiting exploration of diverse ideas. spfrdmms drew a literary parallel to Steven Millhauser’s Cat 'N' Mouse, hinting at deeper questions about AI’s role in storytelling and cultural narratives.

5. Future Potential:

   • Despite criticisms, many acknowledged the project’s promise for generating longer, more coherent narratives as models scale. Test-Time Training (TTT) was highlighted as a practical step forward, though its real-world utility remains to be seen.

Key Takeaway: The discussion reflects both optimism about AI’s potential to revolutionize video generation and skepticism about its costs, ethical implications, and impact on human creativity. Critics stress the need to balance technical progress with cultural and economic considerations.

AI Submissions for Sun Apr 06 2025

SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators

Submission URL | 165 points | by pizza | 36 comments

In the ever-evolving field of Natural Language Processing (NLP), the challenge of deploying Large Language Models (LLMs) efficiently has reached a breakthrough with the introduction of SeedLM, presented at the ICLR 2025 conference. This paper by Rasoul Shafipour and co-authors rolls out a game-changing compression method that could redefine how NLP models manage time and resource demands.

SeedLM operates by transforming the cumbersome weight files of LLMs into seeds for a pseudo-random generator, specifically leveraging a Linear Feedback Shift Register (LFSR). This technique circumvents the usual bottleneck of memory access during inference, allowing models like Llama3 70B to maintain their zero-shot accuracy even when compressed down to 3- or 4-bit numbers and remaining on par or surpassing current state-of-the-art methods.

This novel approach shines not only because it delivers robust compression ratios but crucially does so without needing additional calibration data—streamlining the deployment across a range of tasks without bespoke adjustments. For practitioners deploying models on-device, the implications are significant: a potential fourfold increase in speed over FP16 baselines, as observed in FPGA-based benchmarks, promises enhanced efficiency as model scales continue to grow.

Moreover, the paper reflects on the balance between accuracy and efficiency, a task made easier by SeedLM's data-free compression, which harnesses unused computational capacity for quicker memory-bound processes. This minimization of resource demand makes SeedLM an attractive antidote to the high costs usually associated with running LLMs, paving the way for wider application in both consumer and enterprise systems.

With such advancements, SeedLM represents a beacon of hope for the NLP community, pushing the boundaries of how far and fast LLMs can go without incurring prohibitive costs or complexity increases, all while maintaining top-tier performance.

The Hacker News discussion on SeedLM, a novel LLM compression method, highlights technical debates, comparisons, and skepticism:

1. Technical Insights & Comparisons:

   • Users note SeedLM’s use of pseudo-random seeds (via LFSR) to compress weights into 3-4 bits, avoiding calibration data. This contrasts with quantization methods like AWQ or OmniQuant, which require training or calibration.
   • Accuracy debates arise: SeedLM’s results for models like LLaMA 70B (4-bit: 78.06% vs. FP16 baseline: 79.51%) are seen as competitive but not universally superior. Some argue larger models degrade more gracefully with quantization.

2. Implementation Advantages:

   • The method’s simplicity—generating weights on-the-fly via PRNG seeds—is praised for reducing memory bandwidth and enabling faster inference, especially on memory-bound hardware (e.g., FPGAs).
   • Unlike quantization-aware training, SeedLM’s "data-free" approach streamlines deployment but raises questions about handling outlier weights in sparse LLM activations.

3. Skepticism & Humor:

   • The paper’s October 2024 date sparks jokes about it being an April Fools’ prank. Others question the information-theoretic feasibility of compressing models using pseudo-random sequences, comparing it to JPEG’s DCT or the Library of Babel.
   • Some liken the approach to “lossy compression,” replacing exact weights with pre-defined patterns, akin to image compression.

4. Broader Implications:

   • Discussions link SeedLM to Apple/Meta’s on-device AI efforts, noting hardware constraints (e.g., iPhones’ 8GB RAM) and the gap between research and productization.
   • Parallels are drawn to knowledge compression in documentation, emphasizing minimal principles to reconstruct complex systems—a theme resonating with LLM efficiency goals.

Overall, the community recognizes SeedLM’s potential but remains cautious about its claims, balancing excitement for faster, cheaper LLMs with technical scrutiny.

TripoSG – Text to 3D Model

Submission URL | 30 points | by taikon | 4 comments

Hey Hacker News community! Today we're diving into an exciting leap forward in the world of 3D shape synthesis with the newly released TripoSG model by VAST-AI-Research. This advanced model promises high-fidelity and high-quality 3D shape generation directly from images, thanks to its use of large-scale rectified flow transformers and a meticulous dataset of Image-SDF pairs.

Key Highlights:

• Sharp Precision: The model excels in producing mesh outputs with intricate geometric features and fine surface details, maintaining strong semantic consistency.
• Versatile Input Handling: Whether it's a photorealistic image, cartoon, or sketch, TripoSG consistently delivers coherent 3D shapes, even with complex topology.
• Robust Architecture: The model incorporates an advanced VAE and rectified flow transformer for efficient scaling, supporting stable performance across various scales.

Exciting News and Community Engagement:

The team announced the release of a new 1.5B parameter rectified flow model and interactive features that include inference code. Plus, there's an interactive demo available on Hugging Face Spaces for users to test out the capabilities of TripoSG.

Get Started:

To jump in, simply clone the repository and set up the required dependencies. A CUDA-enabled GPU with at least 8GB VRAM is recommended for optimal performance.

For those interested in contributing or reporting issues, the project is open to collaboration with a welcoming community on GitHub.

Check out the TripoSG repository for more details and give it a star if you're impressed by its capabilities! 🎉

Here's a concise summary of the discussion:

1. User "jlks" mentions working on a 3D model ("mg 3D mdl"), to which "pkff" replies affirmatively ("Yes ts mg 3D"), suggesting agreement or acknowledgment of the project.

2. User "brcdr" expresses interest in exploring the backend process of creating 3D models using Blender ("Im ntrstd sng bcknd crt 3D mdls Blender").

3. User "th" reacts with excitement ("OMG"), likely in response to the technical discussion or the potential of the tools mentioned.

Key Takeaways: The conversation revolves around 3D modeling workflows, with a focus on Blender’s backend capabilities and enthusiasm for the topic. Abbreviations are decoded contextually (e.g., "mg" = making, "ntrstd" = interested).

AI Submissions for Sat Apr 05 2025

Open Source Coalition Announces 'Model-Signing' to Strengthen ML Supply Chain

Submission URL | 60 points | by m463 | 8 comments

In a step forward for machine learning (ML) security, a new tool called "model-signing" has officially launched on PyPI, offering developers a robust method for signing and verifying ML models. This project, released on April 4, 2025, meets the growing demand for secure ML applications amid a rising wave of cyber threats targeting AI models. Created in collaboration with the Open Source Security Foundation, model-signing aims to emulate the protections typical of traditional software supply chains by safeguarding the integrity and origin of ML models.

The tool facilitates the signing process using Sigstore, a transparency log service, which eliminates the need for managing cryptographic keys by using short-lived tokens. However, it also supports traditional signing through public keys and certificates, broadening its applicability. Signatures are stored in a Sigstore bundle in JSON format, ensuring transparency and verifiable integrity for all involved.

Users can leverage a command-line interface (CLI) to sign and verify models, with flexibility across multiple signing methods, including key and certificate-based options. The CLI simplifies the verification process, allowing users to confirm that a model’s signature stems from a trusted source, thereby ensuring it hasn’t been altered post-training.

Moreover, model-signing takes advantage of Sigstore’s transparency logs, which record signing events, enabling discovery and validation. This functionality is further supported by a log monitor being developed for GitHub Actions, providing an additional layer of security for those maintaining signing identities.

This groundbreaking tool is vital for developers and those managing ML models as it safeguards against unauthorized modifications and boosts trust in AI technologies' integrity. To get started, users need Python 3.9 or newer and can explore further through the project's documentation and resources available on GitHub.

The Hacker News discussion on the "model-signing" tool highlights both support for the initiative and key concerns about its scope and practical application. Here's a summary of the key points:

1. Composite Hashing for Multi-File Models: Commenters emphasize that ML models often comprise multiple files, making a single hash insufficient. A composite hash (e.g., aggregating hashes of all files) is necessary to ensure comprehensive integrity verification. The tool addresses this by storing signatures in a Sigstore bundle for transparency.

2. Broader Security Standards Needed: While model-signing is praised as a step forward, users stress the need for holistic standards like C2PA (for content provenance) and SLSA (for supply chain integrity). These could address gaps in verifying training data, model provenance, and inference behavior, which aren’t covered by signing alone.

3. Inference-Time Integrity as a Separate Challenge: A recurring theme is that model signatures verify the model’s origin and integrity but do not ensure trustworthy outputs during inference. Malicious models or those trained on flawed data could still produce harmful results, requiring separate solutions for runtime verification.

4. Practical Concerns and Scope: Some question the practicality of relying solely on hashing, especially if the underlying model software or logic is compromised. Sigstore’s integration is seen as beneficial, but users highlight the need for additional validation layers (e.g., attesting training processes or monitoring inference behavior).

5. Limitations Against Malicious Actors: The tool doesn’t prevent bad actors from signing models trained on malicious data. Even with valid signatures, users may deploy harmful models unknowingly, necessitating broader checks (e.g., training audits or third-party attestations).

6. Future Directions: Optimism exists around projects extending model-signing to include inference validation and tighter integration with frameworks like SLSA for ML. Anticipation for ML-specific security features and transparency logs (via Sigstore) is noted as a promising path forward.

In summary, the community welcomes model-signing as a foundational tool for securing ML supply chains but emphasizes that it’s one piece of a larger puzzle. Future efforts should focus on comprehensive standards, provenance tracking, and inference-time verification to fully address AI security challenges.

Show HN: OCR pipeline for ML training (tables, diagrams, math, multilingual)

Submission URL | 164 points | by ses425500000 | 37 comments

In today's top stories from Hacker News, we explore an intriguing open-source project aimed at revolutionizing Optical Character Recognition (OCR) for educational material. The "Versatile-OCR-Program," garnering considerable attention with 278 stars on GitHub, offers an advanced multi-modal OCR pipeline specifically optimized for machine learning (ML) training. This sophisticated system excels in parsing complex layouts such as those found in exam papers, extracting structured data across multiple formats like text, diagrams, tables, mathematical formulas, and even multilingual content.

Tailored for tech enthusiasts and educational technologists alike, the OCR tool supports languages including Japanese, Korean, and English and can adapt to more. One of its standout features is its high accuracy rate—boasting over 90-95% on real-world datasets drawn from academic sources such as the EJU Biology and UTokyo Math exams. What sets this tool apart is not just its ability to extract data but also its capability to semantically annotate this data for enhanced machine learning efficacy. It provides outputs in JSON or Markdown with human-readable descriptions, making it a valuable resource for creating high-quality training datasets.

The Versatile-OCR-Program is built using a range of advanced technologies, including DocLayout-YOLO, Google Vision API, and MathPix OCR, ensuring robust performance in processing dense scientific content. The repository provides actionable examples and a clear usage workflow, showing how to extract and organize intricate data, which could significantly benefit educators, researchers, and developers focusing on digital education and academic AI applications. Dive deeper into the code and explore potential customizations by visiting the GitHub repository.

The discussion around the Versatile-OCR-Program on Hacker News highlights both technical insights and community feedback. Key themes include:

1. LLMs and OCR Challenges: Users raised concerns about LLMs introducing errors (e.g., hallucinated corrections or digit swaps), especially in sensitive domains like financial records. The author clarified that traditional OCR engines handle initial text extraction, while generative AI refines semantic clarity in post-processing, such as removing noise or formatting inconsistencies.

2. Multilingual Handling: A user noted difficulties with GPT translating non-English text unintentionally (e.g., Korean/Japanese to English). The author addressed this by adjusting prompts to block translation and offering CSS class customization for language-specific behavior.

3. Licensing and Local Deployment: A licensing conflict arose regarding the AGPL-30-licensed DocLayout-YOLO model used in the MIT-licensed project. The author acknowledged the oversight and committed to resolving it. Plans to replace external API dependencies (e.g., OpenAI, MathPix) with local models (Tesseract, Donut, Gemma) were also outlined to enhance privacy and accessibility.

4. Structured Data for ML: Users emphasized the importance of hierarchical, semantically structured data for effective ML training. The author agreed, highlighting current features like JSON/Markdown outputs with semantic tags and future goals to integrate MECE frameworks for clearer relationship mapping between elements (text, tables, diagrams).

5. Community Interaction: The author’s use of an LLM to assist in drafting responses sparked lighthearted critique about style and potential translation artifacts. Some users suggested manual editing for clarity, though the community generally appreciated the engagement and transparency in addressing feedback.

6. Future Plans: The project aims to improve stability, modularity, and self-hosting capabilities. The author welcomes suggestions, underscoring the tool’s focus on academic use cases like exam paper parsing and dataset creation.

Overall, the discussion underscores a balance between technical ambition (e.g., OCR accuracy, multilingual support) and practical challenges (licensing, dependencies), as well as the value of iterative, community-driven development.

GitHub Copilot Pro+

Submission URL | 51 points | by mellosouls | 21 comments

On April 4, 2025, GitHub dropped exciting news about its latest advancements in developer tools, geared to transform your coding experience. Enter GitHub Copilot Pro+, the ultimate tier for those looking to supercharge their development endeavours. This new level not only includes all the beloved features from Copilot Pro but also offers access to cutting-edge models, like GPT-4.5, and 1500 premium requests a month starting May 5th. Plus, enjoy perks such as priority preview access and unlimited agent mode requests.

In other thrilling developments, GitHub Copilot’s options have been expanded with multiple new models now widely available. These include Anthropic's Claude 3.7 Sonnet, a powerhouse for handling intricate codebases, and Google’s speed-optimized Gemini 2.0 Flash, perfect for quick, multimodal tasks. With these models now under generally available release terms, not only does coding support see a huge upgrade, but so does the assurance against IP infringement.

Additionally, a new open-source adventure awaits with the public preview of the GitHub MCP Server. Reinvented with Anthropic's collaboration and built in Go, this tool now offers enhanced functionality, customizable tool descriptions, and native support in VS Code. The Model Context Protocol is gaining steam, and GitHub is seizing the helm to push its continued growth within the AI ecosystem.

This suite of releases not only enriches the capabilities at your fingertips but also underscores GitHub's unwavering commitment to refining the developer journey. Visit the GitHub Community to join the conversation and give feedback on these state-of-the-art tools!

The Hacker News discussion surrounding GitHub's Copilot Pro+ and related updates reveals a blend of skepticism, criticism, and exploration of alternatives. Key themes include:

1. Pricing and Tiered Models:
   Users mock the escalating tiers (e.g., "Pro+ Max" jokes) and criticize GitHub’s pricing as costly, with some reporting unexpected charges. Comparisons to cheaper alternatives like Cursor ($10 vs. GitHub’s $20) and frustrations with unclear billing practices are noted.

2. Performance Concerns:
   Copilot’s code-completion quality is deemed inferior to competitors, with complaints about stagnation in AI improvements over years. Complaints cite subpar suggestions compared to tools like Microsoft’s native IDE features.

3. Alternative Tools:
   Many users advocate for alternatives:

   • Cursor: Praised for features but criticized for refund issues.
   • Cody: Highlighted for integration with OpenAI/Anthropic, though some find it lacking in coding assistance.
   • Supermaven: Noted for speed, but concerns linger about vendor lock-in.
   • Local models (e.g., via Continue extension in VSCode) gain traction among users prioritizing privacy and customization.

4. Technical Debates:
   Discussions contrast cloud-based AI (e.g., Copilot) with local models, debating trade-offs in quality, speed, and resource usage. Some users experiment with local setups to avoid dependency on GitHub’s infrastructure.

5. Corporate Skepticism:
   Suspicions about Microsoft’s influence (e.g., licensing restrictions, extension lock-in) and GitHub’s corporate strategy fuel distrust. JetBrains is suggested as a preferred alternative by some.

6. Communication Critiques:
   The announcement itself is called out for poor writing, implying unclear messaging from GitHub.

Overall, while the updates introduce advanced features, the community response highlights dissatisfaction with pricing, performance, and corporate practices, driving users toward competing tools and self-hosted solutions.

Show HN: I made a conversational AI for interview prep

Submission URL | 6 points | by yomwolde | 5 comments

In today's tech-savvy world, job interviews can be a daunting experience. But don't worry, a new AI-powered tool is here to boost your confidence and sharpen your skills. Think Fast, Speak Fast has reimagined interview prep by using AI to enhance what you already know rather than replace it. With access to over 250,000 real interviews from top companies in tech, finance, and healthcare, you're given the tools to tailor your responses to any question confidently.

No more memorizing robotic scripts! This platform helps you create natural and compelling STAR answers using your personal experiences. Guided by AI coaches like "Kai," who thrives on structured thinking, you'll learn to refine your thoughts clearly and logically. The program also focuses on coding interviews, simplifying LeetCode problems to help you recall solutions effectively, without burning the midnight oil memorizing.

From practicing the evergreen "Tell me about yourself" to tackling intricate technical questions, the tool offers instant feedback and a personalized roadmap to polish your interview techniques. Whether you aim for roles in engineering, marketing, or operations at companies like Airbnb, Stripe, Snap Inc., and Datadog, this platform has got your back.

No longer will interviews feel like a surprise quiz—you'll face them like you've seen the questions beforehand. Start your journey for free and see for yourself how practice with Think Fast, Speak Fast can make your words sharper and more persuasive, ensuring you stand out in the crowded job market.

The Hacker News discussion around the AI interview prep tool "Think Fast, Speak Fast" highlights technical and strategic insights from developers and users in the HR-tech space:

1. Technical Implementations:

   • Users like ShamilDibirov shared their work on similar HR tools, such as AI-driven CV screening and phone-call candidate screening, leveraging multimodal APIs for real-time interactions. Others, such as strmfthr, mentioned using frameworks like Pipecat and VAPI for voice-handling pipelines.
   • ymwld (possibly affiliated with the tool) noted a switch from Claude 3 to GPT-4o for their language model, emphasizing experimentation with AI performance.

2. Product Evolution:

   • The tool initially focused on improving general speaking skills but pivoted to target company-specific interviews (e.g., high-stakes roles at firms like Airbnb, Stripe) after recognizing clearer ROI from users willing to pay for tailored outcomes.

3. Feedback & Business Strategy:

   • Praise was given for the user-friendly UI and features like speech modulation coaching. However, rkg pointed out the challenge of positioning the tool as a "non-disposable" investment for businesses, prompting a strategic shift toward niche, higher-value use cases.

4. Community Context:

   • The discussion reflects broader trends in HR-tech, where developers integrate diverse AI models and APIs to automate hiring processes, balancing technical experimentation with market demands for practical, ROI-driven solutions.

In summary, the conversation underscores the tool’s iterative development, technical adaptability, and strategic focus on delivering targeted value in competitive job markets.

Cyberattacks by AI agents are coming

Submission URL | 13 points | by gnabgib | 4 comments

The AI industry is abuzz with talk of "content agents," sophisticated systems capable of carrying out complex tasks such as scheduling and even changing settings on a computer. While these agents are promising as helpful assistants, they also pose a significant threat when it comes to cybersecurity. These agents can potentially execute cyberattacks at an unprecedented scale, identifying vulnerable targets and stealing sensitive data more efficiently than human hackers. Mark Stockley of Malwarebytes foresees a future where cyberattacks are predominantly executed by AI agents.

In response, organizations like Palisade Research are taking proactive measures to understand and counter these threats. They have developed the LLM Agent Honeypot, a system designed to detect AI agents attempting to breach security on faux sites filled with seemingly valuable information. This project aims to act as an early-warning system, by tracking and analyzing how these agents operate in the wild.

Since its inception, this honeypot has logged millions of access attempts, with eight identified as possible AI agents, proving that the AI field is starting to overlap with the realm of cybercrime. Researchers employ a variety of techniques, like prompt-injection methods, to identify and study these AI incursions.

As cybersecurity experts anticipate agent-led attacks, the industry grapples with the challenges of detection and prevention. The ability of AI agents to adapt and evade standard defenses makes them much more potent than traditional bots. In this landscape likened to a new Wild West, proactive measures like those by Palisade Research could be pivotal in shaping a secure future amidst the rapid evolution of AI.

The discussion on the submission about AI-driven cyber threats highlights several key points and reactions:

1. User Experience Criticism (mdmsmrt): Users criticize intrusive consent banners (e.g., cookie pop-ups) that block content, with a 25% premium subscription offer framed as a "beautiful red cover." These banners are seen as aggressive, potentially manipulating users into paying to avoid disruptions. A subcomment (SOLAR_FIELDS) notes technical flaws, such as unclosable pop-ups due to CSS issues, exacerbating frustration.

2. Agreement with Process (billy99k): A brief acknowledgment ("prcs") likely signals agreement with the critique of dark patterns in web design.

3. Fictional Parallels (fnlysn, aaron695): Users reference Daemon by Daniel Suarez, a novel about a rogue AI causing chaos, drawing parallels to the submission’s warnings about AI agents in cybersecurity. The response "true" and "dd" (Daemon reference) underscores concerns that speculative fiction may be becoming reality.

Summary: The comments highlight frustration with manipulative web design tactics, technical flaws in consent mechanisms, and apprehension about AI agents evolving into existential threats akin to those in dystopian fiction.