a quiet week is all we need.

AI News for 12/27/2024-12/30/2024. We checked 7 subreddits, 433 Twitters and 32 Discords (215 channels, and 5832 messages) for you. Estimated reading time saved (at 200wpm): 696 minutes. You can now tag @smol_ai for AINews discussions!

Enjoy the break.

The Table of Contents and Channel Summaries have been moved to the web version of this email: !

AI Twitter Recap

all recaps done by Claude 3.5 Sonnet, best of 4 runs.

TO BE COMPLETED

AI Reddit Recap

/r/LocalLlama Recap

Theme 1. Deepseek's V3: Performance and Critique

• Sam Altman is taking veiled shots at DeepSeek and Qwen. He mad. (Score: 1486, Comments: 432): Sam Altman criticizes DeepSeek and Qwen models, highlighting the simplicity of replicating existing ideas versus the complexity and risk of genuine innovation. His post on Twitter has garnered significant attention with 1.3 million views, 1,175 reposts, 233 quote tweets, 15.2K likes, and 2,046 bookmarks.
  • Many commenters criticize Sam Altman and OpenAI for claiming innovation while relying heavily on foundational research from Google and other open-source contributions, noting that OpenAI's work builds on existing technologies like the Transformer architecture from the paper Attention Is All You Need. They argue that OpenAI has monetized public knowledge while restricting access to its own findings.
  • There is a sentiment that OpenAI's competitive edge or "moat" is questionable, as models like DeepSeek and Qwen are achieving similar performance at lower costs. Commenters highlight the irony of OpenAI's past actions, such as scraping the internet for data without compensation, while now criticizing others for leveraging their work.
  • The discussion includes skepticism about OpenAI's sustainability and innovation claims, pointing out that OpenAI's profitability is challenged by competitors offering similar services cheaper. The conversation also touches on the broader issue of how innovation is often a cumulative process, with companies building on each other's work rather than creating entirely new concepts.

• Deepseek V3 performs surprisingly bad in Misguided Attention eval, which tests for overfitting. (Score: 176, Comments: 49): Deepseek V3 performed poorly in the Misguided Attention evaluation, solving only 22% of the 13 test prompts, indicating significant overfitting issues. The model struggled with prompts involving slight variations of known problems, possibly due to optimizations like the compressed KV cache or MoE, and exhibited repetitive loops, suggesting potential finetuning issues related to reasoning traces.
  • Overfitting and Reasoning Challenges: The discussion highlights Deepseek V3's overfitting issues, with users suggesting that the model's reasoning capabilities could be better evaluated using its DeepThink mode. There is a consensus that the model struggles with variations of known problems, possibly due to biases in pretraining data and finetuning challenges.
  • Misguided Attention and Evaluation Methods: The term "misguided attention" is debated, with some users noting it describes the evaluation issue well. The evaluation of reasoning models is complicated by API limitations, leading to reliance on web interfaces, which can skew results.
  • Model Architecture and Performance: There is speculation about the architecture of various models, with some users noting that Deepseek models are stubborn in task execution, possibly due to MoE architecture. The conversation also touches on the performance of smaller models like o1-mini in specific tasks, indicating varying strengths across different models.

• Many asked: When will we have an open source model better than chatGPT4? The day has arrived. (Score: 204, Comments: 106): Deepseek V3 is claimed to surpass ChatGPT4 as an open-source model, achieving this milestone 1.75 years after ChatGPT4's release on March 14, 2023. The announcement was shared via a link.
  • Deepseek V3's Open Source Status: There is skepticism about Deepseek V3 being truly open source, as it uses the r1-lite model, which isn't available for download. Users express doubt over claims that Deepseek surpasses GPT-4, noting that open-source models have reportedly outperformed GPT-4 for some time.
  • Model Performance and Parameters: The Mixture-of-Experts architecture for Deepseek V3 has 671B total parameters with 37B activated parameters, but users question its real-world performance compared to benchmarks. Discussions highlight the superiority of models like Claude Sonnet 3.5, which is praised for its tone and feedback integration, over GPT-4.
  • Comparative Model Analysis: Users compare various models, such as Qwen2.5-32b and Llama 405b, which reportedly outperform GPT-4 in certain benchmarks and tasks. The conversation also touches on the desire for open-source models with capabilities akin to o1 mini and emphasizes the historical context of GPT-4's performance.

Theme 2. Cerebras's Trillion Parameter Training on CS-3

• 10th December 2024: Cerebras Systems + US Energy Sandia National Labs have CLAIMED to demonstrate training of a 1 trillion parameter model on a single CS-3 system (!) This is ~1% the footprint & power of an equivalent GPU cluster. (Score: 348, Comments: 66): Cerebras Systems and US Energy Sandia National Labs have announced the successful training of a 1 trillion parameter model on a single CS-3 system, claiming it uses only about 1% of the footprint and power compared to an equivalent GPU cluster. For more details, refer to their press release and related posts on CerebrasSystems and SandiaLabs.
  • Wafer Yield and Die Defects: Discussions highlighted skepticism about Cerebras' claims of defect-free dies, referencing historical allowances for defective dies in their products. Calculations suggested that achieving a 99.9954% yield per die is highly improbable, given typical defect densities reported by TSMC.
  • Hardware and Performance: The training was conducted on a cluster of 16 CS-3 chips, not a single chip, which some found misleading. Users pointed out that while the architecture could potentially lower costs by consolidating numerous cores onto a single board, the performance and scalability compared to traditional GPU clusters remain crucial considerations.
  • Cerebras' Market Position: Despite the promising technology, Cerebras hasn't been widely adopted, potentially due to supply issues or the lack of an accessible ecosystem for startups. The discussion also touched on the potential for Cerebras to disrupt Nvidia's dominance if their hardware proves superior and can be easily integrated into existing frameworks like PyTorch.

Theme 3. Affordable Local AI: Performance on Budget GPUs

• Budget AKA poor man Local LLM. (Score: 354, Comments: 76): A Reddit user describes building a budget-friendly local LLM setup using older hardware, including a CROSSHAIR V FORMULA-Z motherboard and 2x P102-100 GPUs, for a total cost of $130. Despite limitations in image generation speed, the setup efficiently runs various models like Phi-4-14B and llama3.2-3b with response times under one second, demonstrating the feasibility of low-cost, performance-oriented AI experimentation.
  • GPU Performance Comparisons: The RTX 3060 12GB is highlighted as a budget-friendly option for AI tasks, with performance metrics showing 12 tokens per second for certain models. Comparatively, the 4060 Ti 16GB achieves 23 tokens per second, indicating a significant performance boost for a modest price increase, as discussed in this Reddit post.
  • Budget Hardware Feasibility: While the setup described in the post costs $130, it may not be generally repeatable at that price, with potential total costs reaching $500 due to additional components. However, using mining GPUs and second-hand components can still create a powerful system for around $200 if deals are found.
  • Community Interest and Experimentation: The post has sparked interest among users wanting to experiment with larger models on a budget. Some users are considering similar setups using older or unused hardware, and there's curiosity about performance in other domains like image classification, although the setup is primarily geared towards LLMs.

Theme 4. SmallThinker-3B: Efficient Reasoning in Small Scale Models

• Introducing SmallThinker-3B-Preview. An o1-like reasoning SLM! (Score: 303, Comments: 58): The SmallThinker-3B-Preview is a new reasoning model finetuned from Qwen2.5-3b-Instruct, designed for edge deployment and as a draft model for QwQ-32B-Preview, offering over 70% speedup in token processing on an NVIDIA 4090. The model uses the QWQ-LONGCOT-500K dataset, with over 75% of samples having output tokens exceeding 8K, and is available for open-source research, though it currently has issues with repetitive outputs.
  • Discussions focused on speculative decoding and its implementation, with users sharing command-line parameters for deploying models using llama-server and vllm. A specific setup involving CUDA_VISIBLE_DEVICES and tensor-parallel-size was mentioned for optimizing speculative decoding with the SmallThinker-3B-Preview model.
  • Comments highlighted the potential of smaller models like SmallThinker-3B-Preview for edge computing, emphasizing their ability to run efficiently on consumer-grade GPUs. Users expressed interest in enhancing these models with retrieval-augmented generation (RAG) capabilities and tools for improved knowledge and reflection.
  • The model's fine-tuning process was discussed, with llama-factory being used and plans to share the training configuration. It was noted that fine-tuning the 3B model could be done with a single NVIDIA 4090 or 3090 GPU, reflecting the model's accessibility for further development.

Other AI Subreddit Recap

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT

Theme 1. OpenAI's O1 Offers Significant Advantage in Math and Education

• O1 is very good at Math and wins the Putnam Exam (Score: 109, Comments: 84): O1 demonstrated exceptional mathematical prowess by scoring 8/12 on the 2024 Putnam Exam, a significant achievement given the exam's difficulty. The correct answers were for problems A1, A2, A3, A4, A6, B3, B4, and B5, while errors occurred on A5, B1, B2, and B6.
  • O1's Performance and Grading: The discussion highlights skepticism regarding O1's reported performance on the 2024 Putnam Exam, with some suggesting that the grading might not align with the rigorous standards of the exam. Kevin Buzzard estimates O1 got one problem right and partial credit on others, as discussed in his blog.
  • Training Data and Exam Timing: There's clarification that the 2024 Putnam exam occurred after the AI's training data cutoff in 2023, suggesting that O1 did not have prior access to the exam content, as confirmed by Science_421.
  • AI's Approach vs. Human Approach: Commenters note that O1 often reaches correct answers without showing all steps, akin to a physicist's approach rather than a mathematician's, who would typically provide a detailed proof. This style is not aligned with the Putnam's grading criteria, which values complete logical reasoning.

• o1 is literally a game-changer! (Score: 126, Comments: 64): O1 significantly enhances the learning experience compared to GPT-4, making complex problem sets more manageable and improving the user's understanding of the process rather than just providing answers. This has resulted in improved academic performance and increased parental approval.
  • Clarification Issues: Users noted that while O1 provides significant improvements over GPT-4 in educational settings, it still struggles with making assumptions and providing incorrect answers without seeking clarification, a common problem across many LLMs. Suggestions included the need for more explicit input requirements to mitigate these issues.
  • Coding Challenges: A user shared an experience where O1 provided incorrect coding information and stubbornly insisted on its correctness despite evidence to the contrary. Switching to 4o resulted in immediate correction and apology, highlighting discrepancies in performance between the two models.
  • Educational Impact: The O1 model is praised for its potential to revolutionize education by providing intelligent assistance in understanding complex subjects, with some users warning against over-reliance on the tool to ensure genuine learning. Concerns were raised about the illusion of improved grades when using LLM aids for problem sets.

• OpenAI, Andrew Ng Introduce New Course on Reasoning with o1 (Score: 116, Comments: 13): OpenAI and Andrew Ng have introduced a new course focused on reasoning with O1, although the post does not provide further details or context.
  • The new course on reasoning with O1 by OpenAI and Andrew Ng is available for free, as highlighted by multiple commenters.
  • Andrew Ng's courses generally receive positive feedback, especially those he personally teaches, though some are criticized for being outdated due to the rapid pace of AI advancements.
  • A direct link to the free course is provided by a commenter: Reasoning with O1.

Theme 2. MAMBA Model's Struggle Against Transformer Dominance

• [D] - Why MAMBA did not catch on? (Score: 134, Comments: 49): MAMBA was anticipated to replace transformers due to its efficiency, offering O(N) complexity during training and O(1) during inference while maintaining comparable accuracy. Despite these advantages, it did not become dominant, possibly due to limitations in state space models or other unaddressed theoretical constraints.
  • MAMBA's Limitations: MAMBA models face practical challenges such as fixed state memory which limits their ability to handle tasks requiring dynamic state tracking, unlike transformers which utilize self-attention for efficient information retrieval. These limitations have been highlighted in theoretical analyses and experiments showing that MAMBA struggles with state tracking and practical copy tasks.
  • Transformer Dominance: The maturity of the software and hardware stack for transformers, including tools like Hugging Face and CUDA optimizations, makes them more accessible and efficient for large-scale applications. This established infrastructure, combined with the high cost of retraining models, deters the adoption of MAMBA despite its potential runtime efficiency advantages.
  • Research and Development: Current research continues to focus on improving transformer architectures, with innovations like Hyena Hierarchy offering significant improvements in efficiency and accuracy over traditional attention mechanisms. This ongoing development and the proven scalability of transformers suggest that alternatives like MAMBA will remain less popular until a major shift occurs in the landscape.

Theme 3. OpenAI's AGI Definition and Economic Metrics

• Leaked Documents Show OpenAI Has a Very Clear Definition of ‘AGI’ (Score: 101, Comments: 62): OpenAI's definition of Artificial General Intelligence (AGI) has been revealed through leaked documents. The details of these documents have not been provided, but the revelation indicates that OpenAI has a specific and clear understanding of AGI.
  • The discussion highlights skepticism about using $100 billion as a benchmark for achieving AGI, with users arguing that financial success does not equate to general intelligence. CarrotcakeSuperSand explains that this metric is tied to a clause in the Microsoft deal, where Microsoft loses rights to OpenAI’s IP upon reaching AGI, thus necessitating a clear financial threshold.
  • Corgis_are_awesome clarifies that the $100 billion figure is related to Microsoft’s initial investment and a 100x cap on their profit, separate from AGI definitions. The OpenAI charter states AGI as an AI system exceeding human capabilities in economically valuable work, with the board having the authority to determine AGI achievement.
  • Class_of_22 and others express confusion and criticism over the perceived arbitrary nature of the profit-based AGI benchmark, with FlugonNine suggesting that the focus on wealth generation reflects the venture capitalist mindset within OpenAI. Cyberdork humorously critiques Sam Altman’s background, attributing the monetary focus to his business-oriented career.

Theme 4. AI's Role in Gaming and Social Media

• Dead Internet Theory is now a corporate objective (Score: 393, Comments: 110): Meta plans to introduce AI-generated characters on Facebook to boost user engagement, allowing interactions that mimic real human interactions through their AI studio. This initiative, reported by the Financial Times, aligns with the broader trend of integrating AI in digital platforms, raising concerns about the authenticity of online interactions.
  • AI Models' Limitations: swagonflyyyy points out the limitations of AI models in conversational contexts, noting that while they excel in utility for backend applications, they often fall short in direct user interactions. Gemma2's 27B model is highlighted as superior for general chatting, and AI's role is better suited for backend tasks like moderation and summarization rather than frontend user interaction.
  • Concerns Over AI Manipulation: AppropriateScience71 and sdmat express concerns over AI being used to manipulate users, citing BlackOps 6's EOMM as a negative example of AI altering game dynamics to enforce outcomes. There is a general sentiment that AI's role in altering user experiences, whether in gaming or social media, is perceived negatively and could harm user engagement.
  • Prevalence of AI on Social Media: Agile-Landscape8612 and OptimismNeeded discuss the widespread presence of AI-generated content on platforms like Facebook, with many users seemingly unaware of it. This suggests that AI-generated posts are already integrated into social media, and banning bots could significantly impact platform content.

AI Discord Recap

A summary of Summaries of Summaries by o1-2024-12-17

Theme 1. AI Models Fight for Coding Supremacy

• DeepSeek V3 Displays Complex Coding Skills: It handles large context windows, excels at tasks like building MTG decks, and outruns some closed-source models. Yet it struggles with “reasoning loops” and XML outputs, showing room for refinement.
• Gemini 2.0 Wins Hearts with Speed: Users praise Gemini’s “flash thinking” for coding assistance, claiming it sometimes beats GPT-4 in speed. They also look forward to Gemini’s upcoming features for specialized tasks like code generation.
• Codeium 2024 Wrapped Confirms New Year Features: The platform offered year-end coding stats while teasing “lots of work left to do” for 2025. Users reported both excitement and frustration over Windsurf outages and credit consumption.

Theme 2. Fine-Tuning & LoRA Legwork

• LoRA Proves Useful but Tricky: Developers argue it retains new knowledge but warn about inflated expectations and dataset pitfalls. Discussions often mention overfitting risks in large-scale pretraining.
• Hymba-1.5B-Instruct Goes Commercial: It draws praise for open-source instruction datasets and “strict batch size requirements,” prompting legal and ethical usage questions. Contributors see it as a stepping stone for robust AI solutions.
• OpenRouter and Aider Integration: Coders encountered ‘model not found’ errors hooking DeepSeek V3 via OpenRouter. Proper environment variables and endpoint settings solved it, enabling streamlined fine-tuning workflows.

Theme 3. Quantization & HPC Performance

• FP8 Tactics Accelerate Transformer Engines: NVIDIA’s FP8 approaches promise smaller numeric footprints with strong accuracy. Users highlight new 2D block quantization from PyTorch’s blog for near 2x speedups.
• TMA vs cp.async Sparks Debate: Fewer threads and registers make TMA more resource-efficient than cp.async. Developers see big gains in HPC tasks, especially GEMM-based workloads.
• 3090 NV-Link & Jetson Orin Nano Face Trials: Multi-GPU bridging intrigues performance seekers, but noise and cost concerns abound. Meanwhile, the Jetson Orin Nano’s 25W mode impresses with modest but functional on-device AI endeavors.

Theme 4. RAG, Embeddings & Agent Workflows

• Local RAG with LlamaIndex: Users feed Excel tables to Llama-3.2 or Llama-3.3, enabling advanced retrieval-augmented generation. Neomagus verifies imported citations to guard against AI hallucinations.
• Light Prompter Shows Efficient Test-Time: It batches prompts for faster model inference, and devs wonder if test time training tweaks model weights too. Others see parallels to RL research for real-time updates.
• Vision Meets Embeddings: Nomic’s nomic-embed-vision-v1 pairs with text embeddings to refine image search. This approach teases multimodal expansions in GPT4All and beyond.

Theme 5. APIs, Pricing & Prompt Engineering

• OpenRouter Users Weigh Costs: Some lament no discounts for input tokens, while performance of models like GPT-4o mini fuels translation-friendly usage. Providers jockey to differentiate with “niche” model strengths.
• Perplexity Pro Baffles Subscribers: DeepSeek v3 is missing despite its touted perks, prompting calls to stick with a free tier. Meanwhile, Reasoning Mode lumps complex queries into structured answers for advanced Q&A.
• Prompt Engineering Gains Structure: Overly broad requests baffle AI code tools, so devs break tasks into smaller steps. People eye “Sora channels” and markdown-friendly spaces for effective knowledge sharing.

PART 1: High level Discord summaries

Codeium (Windsurf) Discord

• Codeium 2024 Wrapped & New Year Roadmap: The team launched Codeium 2024 Wrapped, urging everyone to view and share coding stats in style, followed by a warm year-in-review thank you.
  • They hinted at more features rolling out in 2025, emphasizing lots of work left to do to amp up the user experience.
• Windsurf's Furious Outages & Credit Conundrums: Users reported sluggish responses and 503 errors with Windsurf, prompting some to push for a status page for real-time updates.
  • Frustrations over depleted premium credits led to refund demands and exploration of alternatives like ChatGPT 4o to cope with repeated downtime.
• DeepSeek V3 Dreams Drag On: Impatient chatter arose around the delayed integration of DeepSeek V3 in Windsurf, with users watching rival tools like Cline adopt it sooner.
  • Questions swirled about feature priorities, as some urged Codeium to speed up the merge to keep pace in the AI editor race.
• Context Clutter in Codeium: A lively debate grew around how Codeium handles context length for code revisions, leaving many confused over real limits versus marketing claims.
  • People found persistent issues with maintaining code discussions, even though the platform boasts a high context length for advanced usage.
• React Native SVG Slip-Ups: A user detailed trouble loading SVG icons on native simulators despite flawless web previews, stirring suspicion of version conflicts with react-native-svg and Expo.
  • Community members advocated debugging platform compatibility and library versions before resorting to drastic reconfigurations in their app setup.

Unsloth AI (Daniel Han) Discord

• LoRA Legwork in Fine-Tuning: Members debated whether LoRA is effective for large-scale pretraining, pointing out that careful dataset structuring is crucial to avoid overfitting and inflated expectations (documentation link).
  • They shared previous experiences, acknowledging skepticism over LoRA's reliability for knowledge retention, with references to continued pretraining tips.
• Quantization Quandaries in Llama.cpp: Some users encountered quantization issues with Llama.cpp after recent library updates, causing errors during integration (sample issue report).
  • Discussion focused on missing dependencies and the lack of unsloth quantization for bigger models like Phi 4, highlighting operational delays and library version mismatches.
• Hymba's Hype for Commercial Use: The Hymba-1.5B-Instruct model was introduced with claims of ready-for-commercial usage and strict batch size requirements, as seen on Hugging Face.
  • Contributors pointed out that it was derived from open-source instruction datasets, reminding everyone of legalities and ethical considerations for distributing advanced AI technology.
• Light Prompter Lifts Test-Time Efficiency: The GitHub project Light Prompter showcases batching tactics to increase model inference efficiency, featuring relevant notebooks and code examples.
  • A member mentioned test time training and how it might update weights during inference, with others suggesting it could overlap with RL research yet to be fully explored.

Cursor IDE Discord

• Claude 3.5 Sonnet stirs speculation: Users questioned whether claude-3.5-sonnet differs from claude-3.5-sonnet-20241022, referencing a Cursor forum thread.
  • They noted that claude-3.5-sonnet now redirects to the updated 20241022 build, prompting curiosity over performance gains.
• Composer vs Chat face-off: Some praised the Composer tool for code refinement, even pointing to a discussion on quick 'Fix' actions.
  • Others valued Chat for general guidance, suggesting that a more direct or even frustrated tone occasionally yielded sharper Cursor responses.
• Cursor powers web apps: One person highlighted Cursor’s ease of use by delivering a functional web tool for a mobile MMO game without extensive coding background.
  • Another shared a Guitar Chord Learning App link such as this fretboard tool, underscoring Cursor’s utility for full-stack prototypes.

Stackblitz (Bolt.new) Discord

• Grok’s Great Credit Countdown: With only two days left before the year ends, Grok AI is offering $25 in free credits for its API users, highlighted in this official link, which can be integrated into Bolt projects.
  • Members stressed that these final hours are perfect for trying Grok AI within Bolt, calling it the sweet spot for quick prototyping.
• Voice Prompting Wish in Bolt: A strong push emerged for a voice prompting feature akin to ChatGPT, offering more convenient coding discussions but noting the heavier overhead of audio models.
  • Enthusiasts envisioned hands-free interactions within Bolt, but they anticipated potential cost spikes due to the added model complexity.
• Supabase vs Firebase vs Convex: Database Dilemmas: Developers weighed usage of Supabase, Firebase, or Convex for data hosting in Bolt projects, referencing an open GitHub issue for details.
  • Some highlighted that exporting to StackBlitz enables manual refinements, while others warned that Convex remains in beta and may warrant caution.
• Large Codebase LLM Fatigue: Community members noticed Bolt slowing on extensive codebases, occasionally altering unrelated files, leading to repeated reboots and diff checks.
  • Users recommended reloading projects and toggling diff mode to mitigate random edits, sharing anecdotal success stories that it helped control token usage.

aider (Paul Gauthier) Discord

• DeepSeek V3 Gains Momentum: Many users are switching to DeepSeek V3 for coding tasks, touting large context windows and API docs references. Some users weigh the privacy trade-offs of hosting vs Hugging Face usage, citing cost and context window differences.
  • Others compared it with Gemini for code generation, concluding DeepSeek is faster, especially for extensive projects, while praising the newly introduced Context Caching feature as a cost-saver.
• Aider Installation and Configuration: Enthusiasts emphasize installing Aider globally for stability, referencing official guidelines and specific Python setup steps. Some Arch Linux users give OS-specific tips and note that adjusting .aider.model.metadata.json helps manage context and costs.
  • They also discuss ways to bypass Git restrictions, pointing to GitHub issue #211, while acknowledging the importance of token-limit awareness.
• Gemini 2.0 Excels at Code: Contributors report Gemini 2.0 handles large projects effectively, offering a free tier that helps accelerate coding tasks. They frequent references to model providers on LiteLLM, underscoring performance gains in big codebases.
  • Some rely on Gemini for broad code loading while using specialized models like DeepSeek for final generation, capitalizing on each model’s traits.
• Integrating Aider with OpenRouter: Certain members faced 'model not found' errors when tying OpenRouter to Aider, attributing them to endpoint misconfiguration. They overcame it by enabling specific settings and verifying the correct environment variables, referencing OpenRouter integration tips.
  • Others caution about user privacy with hosted endpoints, but note that once configured properly, Aider can seamlessly invoke DeepSeek via OpenRouter.
• OCR Implementation with TesseractJS: A user showcased building a web app in one hour using Aider, employing TesseractJS for automated OCR tasks. They highlight a boost in productivity from skipping manual coding in favor of direct AI-driven generation.
  • Community members see potential in bridging OCR with code generation, indicating future expansions into advanced text extraction workflows.

Eleuther Discord

• LLM Benchmarking Bloopers: Participants found that LLM performance can be skewed by ambiguous questions, referencing ARC 'Challenge' vs ARC 'Easy' as an example of questionable setups.
  • They recommended shifting to functional tasks over multiple-choice to capture complex reasoning, with open discussion about adopting robust metrics.
• Gradient Routing Gains Ground: Members praised Gradient Routing as a method to isolate model capabilities using data-dependent masks during backprop, referencing a paper about localizing computation.
  • This technique could improve interpretability by mapping specific subregions to certain tasks, fueling insights into advanced debugging.
• TongGeometry's Triumphant Theorems: TongGeometry systematically proposed and solved olympiad-level geometry problems, as described in Proposing and solving olympiad geometry with guided tree search.
  • Some solutions even made it into regional mathematical olympiads, highlighting the model's impressive handling of complex geometric proofs.
• Crosscoders Crack Model Layers: The Crosscoders approach tracks features across multiple layers to better interpret how models evolve representations, referencing an open-source replication.
  • Practitioners hope this method pinpoints nuanced transformations in networks, aiding circuit simplification and direct model diffing.
• Teeny TinyStories Tactics: The TinyStories dataset compiles synthetic short stories for training small LMs under 10 million parameters, per TinyStories: How Small Can Language Models Be.
  • Users reported success in developing simpler architectures without major performance drop, fueling interest in lightweight model design.

OpenRouter (Alex Atallah) Discord

• DeepSeek V3 falters on OpenRouter: Some users reported reduced performance from DeepSeek V3 when using it through OpenRouter, leading to speculation about updates or version changes.
  • They suspect a recent modification or a possible Together API factor may be at play, prompting concerns over consistent performance and user confidence.
• OpenRouter welcomes new LLM providers: Community members noted that integrating models into OpenRouter requires partnerships with established labs or self-hosting, with specialized coding abilities as a strong differentiator.
  • They pointed to Prompt Caching on OpenRouter as a key cost saver and recommended promoting niche strengths to attract user interest.
• GPT-4o mini excels at translations: A discussion on translation models positioned GPT-4o mini as a reliable choice, while Gemini 1.5 Flash was said to produce frequent errors.
  • Users mentioned structured system prompts and relied on the LLM Rankings for translation to optimize their results.
• Multimodal agents spark interest: Developers explored methods for building multimodal agents, clarifying that strict JSON output isn't mandatory for agent workflows.
  • They referenced Anthropic’s guide on building effective agents and mentioned Google’s Project Mariner as a possible inspiration.
• Pricing debates heat up: Community members noticed the lack of input token discounts on OpenRouter, highlighting cost implications for high-volume usage.
  • While some expressed concerns about potential model downgrades, others called for transparent explanations of performance changes.

Nous Research AI Discord

• DeepSeek's Divergent Demo: DeepSeek V3 soared in tasks like building MTG decks via Scryfall queries, ranks #22 on Aidan's benchmark, and impresses with advanced context retention.
  • However, evaluations using MisguidedAttention revealed reasoning loops and contradictory results, fueling questions about its architecture.
• Local AI vs. API: Showdown or Symbiosis?: Members weighed the customization benefits of Aquila's Ollama (ollama.com) and LlamaCPP for local setups, while affirming OpenAI API remains essential for agentic tasks.
  • Others called for more contributors to LlamaCPP, citing its influence across open-source AI projects and highlighting the synergy of local plus API solutions.
• SmallThinker-3B Surprise: The new SmallThinker-3B-preview at Hugging Face shows improved reasoning benchmarks and a knack for systematic steps.
  • Yet, members joked about its inability to stop at the right time, indicating it might overgenerate responses while exploring possibilities.
• Hunyuan's 8GB Gambit: The Hunyuan video model can run on GPUs with only 8GB VRAM, as explained in a blog post, though it proves sluggish at lower resolutions.
  • Community members flagged speed issues, noting that smaller configs open doors for resource-limited setups but may hamper higher-fidelity outputs.
• Metrics That Matter: In binary classification discussions, members championed reporting Precision, Recall, F1, and AUC/ROC from sklearn for added clarity.
  • They stressed the value of a representative test set and urged alignment of metrics with each model’s real-world objectives.

Perplexity AI Discord

• Deepseek v3 Dodges Pro Subscription: Community members noted that Deepseek v3 is conspicuously missing from the Perplexity Pro subscription, prompting confusion about its claimed benefits and higher-level features.
  • Some questioned whether to stick to free Deepseek instead, citing user frustration over paying for Pro yet not seeing advanced functionality.
• Reasoning Mode Ramps Up Complex Queries: Users highlighted Reasoning Mode for detailed Q&A within Perplexity Pro, where it automatically kicks in for intricate queries to improve accuracy.
  • They shared examples of sorting data into tables, underscoring a shared interest in harnessing structured layouts for robust answers.
• Claude 3.5 Sonnet Battles GPT-4O: Multiple users debated performance trade-offs between Claude 3.5 Sonnet and GPT-4O, referencing reliability and latency differences.
  • They pointed out possible synergy with Deepseek or ChatGPT Pro for specialized tasks, stressing that no single model dominates every scenario.
• Searching for API Alternatives & Recency Filters: A user sought Search API solutions that exceed current standards and asked about a custom recency filter, referencing Perplexity API docs.
  • No definitive replies emerged on filter feasibility, spurring community interest in exploring new search paradigms for advanced data retrieval.
• Conversational API Usage Fumbles: Questions arose about whether the Perplexity API can provide context-driven replies instead of dictionary-like definitions.
  • A response confirmed that Sonar models aim for question-answering with proper references, clarifying they are not meant to function as a general conversational agent.

OpenAI Discord

• AI Gen Debates Heat Up: The discussion spanned the pros and cons of image generation tools, referencing the inconsistent results for posters and the varied performance of models like Claude and Eleven Labs.
  • Some participants voiced frustration about heavy cleanup, while others described improvements in audio and video generation workflows, citing a Reddit thread about model unpredictability.
• B-STaR Paper Spotlights Self-Improvement: Members discovered B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners, championing advanced reasoning with minimal human annotation and a self-improvement training approach.
  • A user referenced the Reddit thread to highlight community discussions, suggesting these techniques could enable continuous refinement in future AI logic.
• Gemini 2.0 Gains Grit: Multiple members praised Gemini 2.0 for flash-thinking and coding strengths, particularly its advantage over GPT-4 in speed and integrated usability.
  • They noted it may fill gaps left by OpenAI’s current line-up for specialized tasks, with talk of pushing beyond standard coding assistance.
• Prompt Engineering & Sora Splits: Calls for a dedicated Sora channel intensified, as users wanted more structure around advanced prompt engineering concepts for ChatGPT and related models.
  • Enthusiasts also sought formal prompt engineering courses, acknowledging how rapidly best practices can shift with evolving model updates.
• Token Limits Trigger Tweaks: Members wrestled with GPT-2’s 1024-token limit, while others faced feasibility issues generating lengthy blog posts through OpenAI’s APIs.
  • They discussed chunking content or sampling alternative models, referencing a Discord post for approaches to address token constraints.

Notebook LM Discord Discord

• NotebookLM Audio Adventures: The conversation covers reusing NotebookLM audio publicly with credit, referencing attempts with no adverse repercussions so far and a playful comment that no one has been arrested yet.
  • Some community members encountered inconsistent restrictions on posting YouTube videos and links, attributing it to rate limiting or updated moderation settings.
• Embedding NotebookLM for Interactive Impact: Members proposed embedding NotebookLM on external sites to enable visitor queries, suggesting approaches like scraping or future API connections.
  • They also requested an after the fact record function to preserve critical snippets of a conversation, emphasizing a built-in recording feature for easier reviewing.
• NotebookLM Plus Perks & Limits: Many discussions focused on the 500-notebook cap for Plus users versus 100 on free accounts, referring to NotebookLM Help for clarity.
  • They also mentioned upload errors for MP3 files and coverage gaps in the resulting output, spotlighting system constraints that affect advanced usage.
• Gemini 2.0 Podcast Quirks: The gemini-2-podcast repo demonstrates Python scripts generating Gemini 2.0-based audio, although it ignores new files until the entire audio is deleted and re-rendered.
  • Others noted NotebookLM can skip or misread user sources, fueling interest in official APIs and mobile support to streamline cross-platform access.

Stability.ai (Stable Diffusion) Discord

• M2 Max MacBook Pro sparks performance debate: Engineers questioned whether a M2 Max MacBook Pro with 32GB RAM and a 38-core GPU can tackle local AI workloads effectively, highlighting differences from Nvidia GPU setups.
  • Some found it usable, but others warned that truly heavy tasks could feel subpar on Apple's hardware.
• Depth map fiasco annoys creators: Users ran into banding artifacts when employing depth maps from 3D software, causing the model to interpret unintended edges.
  • They advised adjusting maximum depth levels and sticking to formats aligned with Stable Diffusion requirements.
• LoRa training locks in consistent style: A children’s book illustrator learned to maintain watercolor character designs by training a LoRa in Stable Diffusion.
  • They combined reference photos with specialized LoRa fine-tuning to achieve uniform illustrations.
• AI video creation platforms draw curiosity: Members explored cloud-based solutions like Luma Dream Machine, Kling, and Minimax for quick AI video testing.
  • They discussed cost factors, hardware demands, and shared Webui Installation Guides plus this YouTube walkthrough.
• Discord community wrestles with spam concerns: Several users pushed for stronger moderation tools to counter bot activity and considered censorship implications on model outputs.
  • They worried that stricter safeguards could hinder character generation, especially when handling human anatomy.

Modular (Mojo 🔥) Discord

• Static Mojo vs Python Tradition: Users debated the meaning and usage of static methods in Mojo, worried it might veer from Python's approach.
  • They proposed replicating Python's current behavior for consistency, citing the need to sync with existing rebind documentation at Modular Docs.
• Recursive Struct Showdown: Defining recursive structs with UnsafePointer[Self] triggered segmentation faults in Mojo.
  • A switch to ArcPointer or OwnedPointer offered safer handling, though some overhead was unavoidable.
• Mojo's 'Load' Trick for Faster SIMD: Participants highlighted that using load is better than direct bitcast for handling SIMD data in Mojo.
  • They referenced Performance Notes, underlining how proper memory access is crucial for speed.
• Pointers Parenting Woes: Maintaining child and parent pointers in Mojo's recursive data structures tested users' patience.
  • They championed OpaquePointer as one method to sidestep pointer tangles and optional pointer pitfalls.
• Debug Mode Takes a Dive (#3917): Running Mojo in full debug mode triggered segmentation faults, while normal runtime behaved better.
  • Developers noted issue #3917 would be tackled after holidays, leaving the community waiting for a fix.

LM Studio Discord

• LM Studio Speed Stampede: Users reported up to 20x faster performance hitting 6 t/s using the DeepSeek-V2.5-1210-GGUF model in LM Studio, with Perf Monitor tracking GPU usage.
  • They also referenced a Nomic.ai blog post about real-time scaling in on-device LLMs for code interpreter and tool calling.
• Vision Models Check for Censorship: A user discovered 'censored' Vision Models blocking NSFW content, prompting interest in uncensored approaches.
  • Likewise, they explored advanced functionalities and considered potential workarounds using special configurations.
• 3090 NV-Link & Noise Conundrum: Community members debated NV-Link for dual 3090 setups, questioning if 2x2 bridging beats single cards while juggling longer cables.
  • Others warned about blower fans reaching 83 dB, suggesting water cooling to mitigate noise when running inference tasks.
• Jetson Orin Nano’s 25W Trials: A user tested a Jetson Orin Nano with 20 models in 25W mode, citing a blog post for real-world speed data.
  • Debate followed on quantizing models and optimizing watts-per-token for more compact or edge-based LLM deployments.

GPU MODE Discord

• TMA Takes on cp.async: Participants showed how TMA can outperform cp.async by enabling fewer threads and using fewer registers, thereby cutting resource overhead.
  • They highlighted potential boosts for HPC tasks and pointed to this GEMM series on Hopper GPUs for related examples.
• Power-of-2 Drives MAGVIT-v2: Community members explained how MAGVIT-v2 leverages binary quantization, encoding decimals like 9 as [0][1][0][0][1][0] to represent powers of two.
  • They referenced Dominika Przewlocka-Rus's work suggesting alignment with Laplacian distributions, spurring more conversation on potential bit-shift performance gains.
• ThunderKittens vs Triton Tussle: Members announced ThunderKittens will add integer matmul operators, illustrating ongoing experimentation with custom kernels.
  • They debated whether a carefully tuned TK/CUDA kernel can outpace Triton, citing constraints in Triton's fine-grained async execution and register handling.
• Raspberry Pi 5 GPU Trials: Enthusiasts reported that the Raspberry Pi 5 GPU shows promise with smaller vision workloads despite limited raw compute power.
  • They saw slow performance on larger LLMs using 6–8bit quantization, prompting questions about Vulkan benchmarks and comparisons to Intel CPUs.
• Cracked Tech Jobs in GPU Land: A shared cracked research engineer job highlighted specialized roles in GPU and AI development.
  • The group advised searching for CUDA and Triton keywords, reflecting growing demand for advanced GPU expertise.

Latent Space Discord

• On-Call Chaos: AI Code Woes: One user pointed to this tweet from Shreya Shankar about burdens on on-calls caused by AI-generated code, urging better documentation and testing.
  • Others suggested that devs break tasks into smaller steps so LLMs can manage them effectively, rather than tackling entire complex features blindly.
• Kagi Clash: Searching for an Edge: Users praised Kagi Assistant for its flexible search capabilities, although some noted coverage gaps compared to Perplexity.
  • Enthusiasts look forward to upcoming features including a search API, anticipating stronger competition with similar tools.
• Summit Sparks: 2025 AI Engineering Meetup: An AI Engineering Summit is set for February 20-21, 2025 in New York, reportedly backed by major tech sponsors in prior events.
  • Organizers encourage early pre-registration for special access, promoting a gathering of AI professionals and industry leaders.
• Cursor Conundrum: Collaboration or Chaos?: Multiple devs shared frustration with the Cursor AI coding assistant, describing wasted effort during complex coding tasks.
  • They advised clarifying instructions and using iterative problem statements to reduce friction when pairing with AI tools.

Interconnects (Nathan Lambert) Discord

• Tie at the Top: Chatbot Arena: Chatbot Arena sees OpenAI's o1 jump to a joint #1 spot, earning +24 points from o1-preview and passing other contenders like DeepSeek-V3 at #7.
  • Community chatter highlights Claude's lower ranking as perplexing, with refusals and roleplay issues cited as possible reasons.
• SLMs Contradict The Bitter Lesson: A debate emerged on how smaller language models can excel in targeted tasks by using specialized priors, questioning the push for more data and compute.
  • Participants referenced Llama 3 8B surpassing GPT-3 175B and underscored the importance of domain-specific solutions.
• DeepSeek V3: XML Output Woes & Benchmarks: Members shared frustration that DeepSeek V3 struggles to output XML tags correctly, producing r1-like reasoning instead of fulfilling instructions.
  • They also questioned its instruction-following performance after prompt swaps from V2.5, noting negative feedback on post-training results.
• GRPO vs. Vineppo: RLHF Rivalry: Discussion centered on GRPO (Group Relative Policy Optimization) and its averaging of rewards, contrasted with vineppo's single-sample strategy and mid-episode resets.
  • A user explained that DeepSeek V3 uses GRPO, raising concerns about memory limits with 1b–7b models and the possibility of dropping a value network.
• Gary & Miles Bet on AI's 2027 Trajectory: Community responded to a Gary Marcus post revealing his joint wager with Miles Brundage on future AI achievements.
  • Skeptical remarks included claims that we remain 'insanely far away from 4,' signaling caution about near-term leaps in model capability.

Nomic.ai (GPT4All) Discord

• LLaMA 3.3 in GPT4All Gains Groq Key: Users shared steps for hooking up LLaMA 3.3 (70B) with GPT4All through Groq.com to enable cloud LLM support.
  • They highlighted the cost benefits, noting it spares on-prem hardware overhead for AI workloads.
• Gemini API Support Sparks Excitement: Participants discussed Gemini compatibility with OpenAI’s API and the roadmap for Gemini 2.0, citing google-gemini/cookbook.
  • They expressed interest in using Gemini’s unique capabilities once official GPT4All integration is confirmed.
• Jinja Jitters Trigger Chat Template Woes: Recent GPT4All updates introduced Jinja parsing that caused syntax breakage for older chat templates.
  • Contributors suggested resetting default templates or referencing updated files, encouraging collaborative fixes.
• Vision Embeddings Come Into Focus: Members clarified that nomic-embed-vision-v1 pairs with text embedding models to refine image searches via text queries.
  • They compared Nomic’s vision model to other publicly available options, expecting more robust demos in future releases.
• Ollama Model Exports Spark Talk: Enthusiasts explored reusing Ollama models in GPT4All, referencing the Ollama Model Export Script.
  • They discussed designating Ollama as the LLM engine, pointing to the compatibility it shares with OpenAI-style APIs.

Cohere Discord

• Breathe.ai Signs NDA to Test Cohere: Breathe.ai officially joined Cohere via an NDA, aiming to collaborate on a research prototype.
  • Members welcomed them enthusiastically, sharing hopes for deeper technical exchanges and feedback loops.
• HMM Tokenization Queries Spark Curiosity: Several users asked about HMM (Hidden Markov Model) tokenization techniques, highlighting a gap in shared expertise.
  • No immediate advice surfaced, revealing an interest in expanding knowledge on advanced NLP tokenization methods.
• Cohere's Rate Limit Ruckus: Members encountered a mismatch in expected image embed rate limits, anticipating 400 calls per minute but observing 40.
  • The support team confirmed the rate limit documentation and assured a fix is in progress, reiterating the official cap remains 400 for production keys.
• Fine-Tuning Firefight Continues: A user reported fine-tuning errors, concerned about potential data or configuration issues.
  • Support is investigating delays caused by holidays, promising direct communication and escalating the troubleshooting process.

tinygrad (George Hotz) Discord

• Magnificent Matching Speedup: The claim of an 8x speedup in matching functions sparked intense discussion, citing a bounty bridging 400ms down to 50ms as a target.
  • Skeptics noted that 50% of runtime lies in these functions, spurring talk of how even 2x acceleration might be the more realistic goal.
• Rewrite Rumble: 2.5x Gains, 4/7 Grief: A tweak to full_graph_rewrite yielded a 2.5x boost in model rewrite times, though 4/7 tests promptly broke and called for urgent debugging.
  • Multi-threading emerged as one angle for improvement, alongside smaller test sets for zeroing in on the root issues.
• AM Driver Marathon Aims for 11k Lines: George Hotz pledged to expand the AM driver to 11,000 lines and merge it by year’s end, referencing this commit as a sign of progress.
  • Attendees anticipate Meeting #51 at 930am Monday in San Diego to slash technical debt on scheduler cleanups and push the AM driver onward.
• Tinygrad CUDA Crushes Torch: New benchmarks suggest Tinygrad CUDA is nearly twice as quick as Torch, with OpenCL slicing about 1ms off overhead.
  • The devs recommended using Device[out.device].synchronize() to get precise metrics, noting that JIT speed really kicks in on the third run.
• Frame Evaluation Hook Buzz: Community members highlighted the Frame Evaluation Hook API from PEP 523 as a handy way to capture runs directly in Python.
  • They pointed out that Torch’s dynamo compiler relies on this approach, calling it more flexible than post-capture solutions.

LlamaIndex Discord

• Local Llama-3.2 & Neomagus Secure Legal Citations: Developers discussed building a local RAG app with Llama-3.2 using Llama Index tools to query Excel tables seamlessly.
  • They also highlighted Neomagus for verifying references in AI-generated text, with details shared here, hoping to reduce false citations.
• Llama 3.3 GPU Footprint & Ollama's Role: One user inquired about Llama 3.3 70B GPU requirements, referencing a potential Hugging Face endpoint.
  • Another user tested Ollama locally and saw about 2.77GB of RAM usage running ollama run llama3.3, indicating a more memory-friendly approach.
• Bagel Bakes Monetization for Open Source AI: A representative unveiled Bagel, a platform that helps open source AI developers earn income and sync with Hugging Face.
  • They shared a tweet explaining how this novel architecture keeps developers in control while providing advanced models like Llama-3.3.
• Filtering Nonword Sounds for Audio Clarity: A user explored ahh and um removal using LLMs, sparking interest in refining audio editing workflows.
  • Participants noted that cleaning up filler words could enhance the listening experience for educational and professional recordings.
• LlamaParse API Accelerates Data Manipulation: Members discussed the LlamaParse API for direct integration, showcasing sample calls for uploading and checking parse jobs in official docs.
  • They emphasized the advantage of handling structured data seamlessly, referencing GitHub examples for real RAG scenarios.

LLM Agents (Berkeley MOOC) Discord

• LLM Agents MOOC Reopens for Enrollment: The next LLM Agents course starts in late January, offering sign-ups via this form.
  • Enrollees can reference the upcoming Spring 2025 syllabus as well as the Fall 2024 materials for a head start.
• Certificate Emails Coming in January: Certificates from the earlier LLM Agents MOOC will be emailed by the end of January, though some participants are still waiting.
  • Members confirmed they can access the course website to revisit lecture materials while they wait.

Torchtune Discord

• Dynamo Drama Diminishes: Reports indicate Dynamo errors may be resolved, prompting members to consider removing compiler-disabled settings for better performance.
  • One user recommended verifying speed-ups with both compile modes enabled and disabled, stressing thorough regression checks.
• Flex's Next Frontier Arrives Jan 13: Members anticipate Flex updates in the upcoming 2.6.0 release on January 13, expecting improvements beyond 2.5.1.
  • They noted multiple adjustments had been introduced, hoping these modifications would be integrated before final release.
• Simple Eval vs LM Eval Showdown: A member spotlighted OpenAI's Simple Eval library as a potential alternative to lm eval tools.
  • Debate centered on evaluation speed and compatibility, with participants reviewing the GitHub page for specific implementation details.
• FP8 Feats Propel Transformer Engines: Users discussed FP8 quantization tactics, referencing NVIDIA's Transformer Engine and Microsoft's Automatic Mixed Precision Library.
  • They also highlighted 2D block quantization approaches, citing COAT, PyTorch's Float8 GEMMs blog, and mixed-precision training papers like arXiv:2310.18313 and arXiv:2409.12517.

OpenInterpreter Discord

• OS Mode: Video or No?: A user asked if OS mode can accept video as input, hoping for clarity on its scope.
  • No confirmed solution emerged, but there's growing curiosity about multimedia support.
• Isolation Indecision: Docker vs. OS: Users pointed to the Isolation doc and wondered if it governs operating system locks or Docker and E2B usage.
  • An attached image fueled confusion, suggesting ambiguous terminology in the doc.
• Windows 1.0: Build Me Up: Someone asked about a Windows build for the newly released 1.0 dev version.
  • Cross-platform fans await support to confirm if broad OS compatibility is coming.
• The Great Profile Swap: YAML to PY: Users encountered trouble moving from profiles.yaml in 1.0.0 to the new .py format.
  • They questioned documentation accuracy, worried about saving processes.
• Custom API Base URL Woes: A user hoped to replicate OpenAI-style usage with endpoints like gpt4o or claude-35-sonnet on Ubuntu.
  • They ran into setup hurdles and requested help adapting these custom base URLs.

DSPy Discord

• Arxiv 2412.15563 Gains Eyeballs: One user asked for opinions on Arxiv Paper 2412.15563, seeking clarity on its broader ramifications for large language models.
  • No direct analysis was offered, but there's interest in seeing if it might suit DSPy experiments.
• AI Glossary Gains Momentum: A member introduced an AI Glossary to speed up concept references, citing Generating a Glossary from a Jekyll Blog Using DSPy & Claude as inspiration.
  • They emphasized the interplay between language and technology, noting a backlog of terms still awaiting sharper definitions.
• Openhands Hooks onto DSPy: A question arose about making Openhands a one-shot noninteractive tool that returns chat responses and git diffs, fueling discussion on integrating it into DSPy's pipeline.
  • They recognized potential synergy but pointed out design nuances in how DSPy handles prompt tuning and automation.
• Feedback System Sparks Code Curiosity: A user proposed a system to record feedback on automated code changes for later evaluation, focusing on input/output logging.
  • They plan to use these data points to guide a DSPy pipeline that refines code quality based on historical outcomes.

LAION Discord

• FFmpeg Slicing Gains Traction: One user described a method to gather time stamps then apply FFmpeg to cut video content, praising the clarity of instructions.
  • They voiced satisfaction with the process, calling it a straightforward approach for swift editing.
• Hackathon & Conference Fever in 2025: Someone is seeking suggestions for 2025 hackathons and conferences, already set on ICML, NeurIPS, and CVPR.
  • They want to meet more community members and eagerly invite more ideas.

Gorilla LLM (Berkeley Function Calling) Discord

• Leaderboard Zero-Shot Conundrum: They clarified that recognized models must be tested in a zero-shot environment, yielding a single response with no iterative calls.
  • An API endpoint approach can bypass typical restrictions if the user only calls once, referencing OpenAI’s o1 chain-of-thought logic behind an API.
• Single-Call for Score Security: They stressed that advanced chain-of-thought expansions must remain invisible to the user, enforcing only one API call for leaderboard evaluations.
  • This mechanism keeps the leaderboard consistent by disallowing multi-step generation or repeated attempts within a single evaluation.

The MLOps @Chipro Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The Axolotl AI Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The Mozilla AI Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The HuggingFace Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The AI21 Labs (Jamba) Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

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