[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-2c114f7483e1445f-tradingagents-llm-hedge-fund-sim-w-debating-teams-summary":3,"summaries-facets-categories":105,"summary-related-2c114f7483e1445f-tradingagents-llm-hedge-fund-sim-w-debating-teams-summary":3690},{"id":4,"title":5,"ai":6,"body":13,"categories":60,"created_at":62,"date_modified":62,"description":53,"extension":63,"faq":62,"featured":64,"kicker_label":62,"meta":65,"navigation":86,"path":87,"published_at":88,"question":62,"scraped_at":89,"seo":90,"sitemap":91,"source_id":92,"source_name":93,"source_type":94,"source_url":95,"stem":96,"tags":97,"thumbnail_url":62,"tldr":102,"tweet":62,"unknown_tags":103,"__hash__":104},"summaries\u002Fsummaries\u002F2c114f7483e1445f-tradingagents-llm-hedge-fund-sim-w-debating-teams-summary.md","TradingAgents: LLM Hedge Fund Sim w\u002F Debating Teams",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",5460,1737,17784,0.001938,{"type":14,"value":15,"toc":52},"minimark",[16,21,25,28,32,35,39,42,45,49],[17,18,20],"h2",{"id":19},"multi-agent-structure-mirrors-real-trading-firms","Multi-Agent Structure Mirrors Real Trading Firms",[22,23,24],"p",{},"TradingAgents breaks a hedge fund into specialized LLM agents: four parallel analysts (fundamentals pulls filings for ratio analysis and intrinsic value; sentiment scores Reddit\u002FX mood; news tracks macro events; technical runs MACD\u002FRSI\u002FBollinger Bands), producing independent reports without vector collapse to preserve disagreement as signal. Bullish and bearish researchers debate analyst outputs over configurable rounds, citing specifics. Trader proposes timing\u002Fposition size, risk team checks volatility\u002Fliquidity, and portfolio manager approves\u002Frejects with explanation. Built on LangGraph for node-based orchestration with checkpoint resume on crashes and persistent markdown decision log that injects past trade reflections (alpha vs. SPY benchmark) into future prompts, enabling learning from realized returns.",[22,26,27],{},"This traceable design outperforms mechanical rule-based systems (e.g., moving averages) or opaque ML black boxes by logging full transcripts—analyst reports, debates, rejection reasons—for auditability absent in traditional quants.",[17,29,31],{"id":30},"bull-bear-debate-drives-defensible-positions","Bull-Bear Debate Drives Defensible Positions",[22,33,34],{},"Hedge funds succeed via team arguments, not solo picks; TradingAgents replicates this with structurally opposing researchers who argue multiple rounds on analyst data. Bull pushes open positions, bear counters, trader synthesizes transcript for trade proposal. This preserves diverse signals from parallel analysts, turning conflict into robust reasoning. Portfolio uses 5-tier scale (buy\u002Foverweight\u002Fhold\u002Funderweight\u002Fsell) consistently with research\u002Ftrader outputs and log.",[17,36,38],{"id":37},"painless-setup-and-v024-production-upgrades","Painless Setup and v0.2.4 Production Upgrades",[22,40,41],{},"Clone repo (53k stars, 9.7k forks, Apache 2.0), pip install, set LLM API key (supports OpenAI GPT, Gemini, Claude, Grok, DeepSeek, Qwen, Ollama\u002Flocal). CLI picks ticker, date, provider, debate rounds; runs simulated exchange backtest. v0.2.4 (Apr 25) adds Pydantic-structured outputs for research\u002Ftrader\u002Fportfolio (cuts failures), DeepSeek\u002FQwen\u002FGLM\u002FAzure support, Docker multi-stage builds—drops setup to ~10min for hobbyists.",[22,43,44],{},"Quant researchers get LangGraph reference for multi-agent graphs; fintech founders fork for retail tools; indie hackers study practical agent wiring.",[17,46,48],{"id":47},"key-trade-offs-research-tool-not-live-trading","Key Trade-offs: Research Tool, Not Live Trading",[22,50,51],{},"Token-intensive (4 analysts + debates\u002Ftrader\u002Fmanager per ticker) burns LLM costs; simulated backtest lacks live broker—build your own. Not financial advice; don't bet retirement. Yet weekly releases, multi-lang docs, UCLA arXiv paper (2412.20138) validate as clean 2026 agent benchmark—clone to dissect wiring.",{"title":53,"searchDepth":54,"depth":54,"links":55},"",2,[56,57,58,59],{"id":19,"depth":54,"text":20},{"id":30,"depth":54,"text":31},{"id":37,"depth":54,"text":38},{"id":47,"depth":54,"text":48},[61],"AI & LLMs",null,"md",false,{"content_references":66,"triage":81},[67,72,78],{"type":68,"title":69,"url":70,"context":71},"tool","TradingAgents","https:\u002F\u002Fgithub.com\u002FTauricResearch\u002FTradingAgents","recommended",{"type":73,"title":74,"author":75,"url":76,"context":77},"paper","arXiv:2412.20138","UCLA","https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.20138","cited",{"type":68,"title":79,"context":80},"LangGraph","mentioned",{"relevance":82,"novelty":83,"quality":83,"actionability":83,"composite":84,"reasoning":85},5,4,4.35,"Category: AI & LLMs. The article provides a detailed overview of TradingAgents, a simulation that uses LLM agents to replicate hedge fund decision-making, addressing practical applications of AI in finance. It offers insights into the multi-agent structure and how it enhances trading strategies, which is relevant for builders looking to implement AI in financial products.",true,"\u002Fsummaries\u002F2c114f7483e1445f-tradingagents-llm-hedge-fund-sim-w-debating-teams-summary","2026-04-28 19:30:04","2026-05-03 16:59:26",{"title":5,"description":53},{"loc":87},"2c114f7483e1445f","AI Summaries (evaluation playlist)","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9FoEsXNGLwI","summaries\u002F2c114f7483e1445f-tradingagents-llm-hedge-fund-sim-w-debating-teams-summary",[98,99,100,101],"agents","llm","python","open-source","TradingAgents simulates a Wall Street firm using LLM agents—4 parallel analysts, bull\u002Fbear debaters, trader, risk, and portfolio manager—for fully traceable stock decisions that learn from past 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Beats TensorRT-LLM 9-11% on Agentic Coding Inference",{"provider":7,"model":8,"input_tokens":3695,"output_tokens":3696,"processing_time_ms":3697,"cost_usd":3698},6300,1652,21300,0.00157915,{"type":14,"value":3700,"toc":3722},[3701,3705,3708,3712,3715,3719],[17,3702,3704],{"id":3703},"tackling-agentic-inference-bottlenecks","Tackling Agentic Inference Bottlenecks",[22,3706,3707],{},"Agentic coding systems like Claude Code, Codex, and Cursor push inference engines with contexts over 50K tokens across dozens of turns, stressing per-GPU tokens-per-minute (TPM) for multi-user scaling and per-user tokens-per-second (TPS) for responsiveness (target floor: 70 TPS, up to 200+ TPS). Public benchmarks miss this dual pressure, so TokenSpeed (MIT-licensed preview from LightSeek Foundation) prioritizes both metrics via specialized architecture, avoiding generic chat optimizations.",[17,3709,3711],{"id":3710},"architectural-edges-for-speed-and-safety","Architectural Edges for Speed and Safety",[22,3713,3714],{},"TokenSpeed builds on five subsystems: (1) Compiler-backed SPMD modeling auto-generates collective ops from I\u002FO annotations, skipping manual comms code. (2) Scheduler splits C++ control plane (FSM with type-enforced KV cache ownership\u002Ftransfers for compile-time safety) from Python execution plane (fast iteration). (3) Pluggable kernel layer with registry supports heterogeneous accelerators; its MLA kernel (grouping q_seqlen\u002Fnum_heads for Tensor Core fill, tuned binary prefill softmax) beats TensorRT-LLM decode\u002Fprefill, adopted by vLLM. (4) Safe KV reuse restrictions. (5) SMG for low-overhead CPU-GPU handoff. These cut KV errors (common pitfall) and enable modular accel support beyond NVIDIA.",[17,3716,3718],{"id":3717},"benchmark-dominance-on-real-workloads","Benchmark Dominance on Real Workloads",[22,3720,3721],{},"On NVIDIA B200 with SWE-smith traces (production-like coding agent traffic) and Kimi K2.5 model, TokenSpeed in Attention TP4 + MoE TP4 config tops TensorRT-LLM Pareto: 9% faster at batch=1 min-latency (>70 TPS\u002Fuser), 11% higher throughput at ~100 TPS\u002Fuser. Decode MLA folds query-seq into head axis for better BMM tile fill; binary prefill tunes softmax. With speculative decoding + long prefix KV at batches 4\u002F8\u002F16, latency nearly halves vs. TensorRT-LLM. Single-node only for now; PD disagg coming.",{"title":53,"searchDepth":54,"depth":54,"links":3723},[3724,3725,3726],{"id":3703,"depth":54,"text":3704},{"id":3710,"depth":54,"text":3711},{"id":3717,"depth":54,"text":3718},[61],{"content_references":3729,"triage":3737},[3730,3733],{"type":68,"title":3731,"url":3732,"context":80},"TokenSpeed","https:\u002F\u002Fgithub.com\u002Flightseekorg\u002Ftokenspeed",{"type":3734,"title":3735,"url":3736,"context":71},"other","LightSeek TokenSpeed Technical Details","https:\u002F\u002Flightseek.org\u002Fblog\u002Flightseek-tokenspeed.html",{"relevance":83,"novelty":3738,"quality":83,"actionability":3738,"composite":3739,"reasoning":3740},3,3.6,"Category: AI & LLMs. The article discusses a new open-source LLM inference engine, TokenSpeed, which addresses specific performance issues in agentic workloads, directly relevant to AI engineers and developers. It provides insights into architectural improvements and benchmarks, but lacks detailed implementation guidance for practical application.","\u002Fsummaries\u002F138f159d6a0dc547-tokenspeed-beats-tensorrt-llm-9-11-on-agentic-codi-summary","2026-05-07 22:03:47","2026-05-08 11:28:23",{"title":3693,"description":53},{"loc":3741},"138f159d6a0dc547","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F07\u002Flightseek-foundation-releases-tokenspeed-an-open-source-llm-inference-engine-targeting-tensorrt-llm-level-performance-for-agentic-workloads\u002F","summaries\u002F138f159d6a0dc547-tokenspeed-beats-tensorrt-llm-9-11-on-agentic-codi-summary",[99,98,101],"TokenSpeed open-source engine optimizes agentic workloads with long contexts (>50K tokens) and multi-turn convos, delivering 9% lower latency and 11% higher throughput than TensorRT-LLM at 70-100 TPS\u002Fuser on NVIDIA B200.",[],"iEELj0BdlJHttBF4chi4fvtEa11ZZu5n0Lg0DMzVIIg",{"id":3755,"title":3756,"ai":3757,"body":3762,"categories":3866,"created_at":62,"date_modified":62,"description":53,"extension":63,"faq":62,"featured":64,"kicker_label":62,"meta":3867,"navigation":86,"path":3881,"published_at":3882,"question":62,"scraped_at":3883,"seo":3884,"sitemap":3885,"source_id":3886,"source_name":3876,"source_type":94,"source_url":3887,"stem":3888,"tags":3889,"thumbnail_url":62,"tldr":3890,"tweet":62,"unknown_tags":3891,"__hash__":3892},"summaries\u002Fsummaries\u002F9cf4eabf30c8f73e-open-source-ai-innovation-engine-or-security-risk-summary.md","Open Source AI: Innovation Engine or Security Risk?",{"provider":7,"model":8,"input_tokens":3758,"output_tokens":3759,"processing_time_ms":3760,"cost_usd":3761},8403,2068,28390,0.00242375,{"type":14,"value":3763,"toc":3859},[3764,3768,3771,3774,3778,3781,3784,3787,3791,3794,3797,3800,3804,3807,3810,3813,3832,3836],[17,3765,3767],{"id":3766},"open-source-as-ais-innovation-backbone","Open Source as AI's Innovation Backbone",[22,3769,3770],{},"Gabe Goodhart champions open source as the core of AI progress, arguing that AI's science—math and tensors—is unusually close to usable products, unlike past tech waves. This tight coupling means open source hosts most real innovation, with examples like linear attention mechanisms enabling better long-context models through collaborative tweaks (e.g., hybridizing recurrent linear layers with attention). Martin Keen reinforces this, noting open source underpins even closed frontier models via standards like Anthropic's Model Context Protocol (MCP), donated to Linux Foundation, and agent skill specs like skill.md. These allow open-weight models (Mistral, Llama, Deepseek) or closed ones to invoke services uniformly. All panelists concur: open source democratizes access, accelerates catch-up to frontier capabilities, and fosters architectures anyone can run on their hardware, bypassing lab gatekeeping.",[22,3772,3773],{},"Jeff Crume, the security skeptic, doesn't dispute innovation benefits but tempers hype. He invokes Kerckhoffs' principle from cryptography: only keys should be secret, not algorithms, as scrutiny strengthens systems. Yet he cautions Linux's history—once claimed malware-proof—shows open source isn't secure by default. Consensus emerges: open source thrives on 'a thousand eyes' for innovation, but scale (billions of parameters) overwhelms full vetting.",[17,3775,3777],{"id":3776},"secure-vs-securable-core-security-distinction","Secure vs. Securable: Core Security Distinction",[22,3779,3780],{},"Jeff Crume draws a sharp line: open source AI is 'securable' (design allows fixes) but not 'secure' without deliberate controls. Proprietary claims of inherent security fare no better; security stems from implementation, not source status. Transparency builds trust, essential for AI, but latent bugs in decades-old open code prove even crowds miss flaws. AI itself aids detection—scanning source or reverse-engineering binaries via LLMs\u002Fdecompilers, a capability predating gen AI but now amplified.",[22,3782,3783],{},"Gabe echoes: AI stacks mirror Linux\u002FKubernetes—composable open projects with attack surfaces needing updates and policies. Open code enables fixes, but poor projects emit 'vibe code Spidey sense' (unmanaged security). Martin highlights hybrid realities: closed models rely on open foundations, blurring lines. Divergence: Gabe sees open weights accelerating science (e.g., attention innovations), while Jeff notes proprietary guardrails (pre\u002Fpost-model filters) block misuse—open weights invite 'obliteration' of safety layers via embedding tweaks.",[22,3785,3786],{},"Panel agrees on trust: opacity breeds blind faith, not security. Open source invites scrutiny, but demands proactive policy.",[17,3788,3790],{"id":3789},"model-access-bad-actors-and-emerging-threats","Model Access, Bad Actors, and Emerging Threats",[22,3792,3793],{},"Debate heats on access: frontier models (e.g., latest from labs) gatekeep via approvals\u002Fconsortia, while open models on Hugging Face run anywhere. Martin predicts open source will close gaps quickly. Jeff worries bad actors access simultaneously—security through obscurity fails, as leaks inevitable. Yet open weights expose more: attackers strip refusals, unleashing unfiltered capabilities.",[22,3795,3796],{},"Gabe ties to agents: autonomy turns agent loops into code interpreters where 'the internet is your untrusted code.' Textual inputs, once filtered by humans\u002Fprograms, now trigger actions via tools—massive attack surface. OpenClaw-like systems exemplify chaos. Jeff nods to AI's dual role: vulnerability scanner and exploit amplifier (reverse-engineering binaries). Martin\u002F Gabe stress context layers (beyond models\u002Fsoftware) compound risks.",[22,3798,3799],{},"Strongest arguments: Pro-open (Gabe\u002FMartin)—stifling access hampers progress; security (Jeff)—scale defeats 'many eyes,' demands controls. No one-size-fits-all; mitigate via guardrails, sandboxes, updates.",[17,3801,3803],{"id":3802},"using-ai-to-secure-ai-and-forward-outlook","Using AI to Secure AI and Forward Outlook",[22,3805,3806],{},"Jeff sees AI securing itself as nuanced: LLMs find code vulns faster than humans, even in proprietary binaries (decompile → scan). Not new—pre-gen AI tools existed—but gen AI scales it. Gabe warns of net-new agent risks, urging trust-boundary rethinking.",[22,3808,3809],{},"Predictions: Open models catch frontiers; innovation via open science\u002Farchitectures unstoppable. Recommendations: Vet projects rigorously; sandbox agents; stay updated; blend open\u002Fclosed (e.g., open standards with closed models). Tradeoffs: Open weights boost utility\u002Finnovation but heighten misuse; closed offers controls at velocity cost.",[22,3811,3812],{},"Notable quotes:",[3814,3815,3816,3820,3823,3826,3829],"ul",{},[3817,3818,3819],"li",{},"Gabe Goodhart: \"Open-source relative to most other innovation waves is where the vast majority of the actual innovation is happening because science by its very nature is open.\"",[3817,3821,3822],{},"Jeff Crume: \"Linux is a good example of a system that is securable, but in and of itself is not necessarily secure.\"",[3817,3824,3825],{},"Gabe Goodhart: \"The agent loop is essentially a code interpreter and the code is literally any text you pass through it... now the internet is your untrusted code.\"",[3817,3827,3828],{},"Jeff Crume: \"Security through obscurity is not an effective model... the only thing about a crypto system that should be secret are the keys.\"",[3817,3830,3831],{},"Martin Keen: \"Open source is foundational to everything in AI now even if we're talking about models that were actually frontier closed models.\"",[17,3833,3835],{"id":3834},"key-takeaways","Key Takeaways",[3814,3837,3838,3841,3844,3847,3850,3853,3856],{},[3817,3839,3840],{},"Prioritize 'securable' open source projects with strong security contribution policies and update cadences—avoid vibe-check fails.",[3817,3842,3843],{},"Distinguish open code (innovation accelerator) from open weights (misuse risk)—use guardrails for models, sandboxes for agents.",[3817,3845,3846],{},"Leverage AI for vuln scanning on open or closed code; reverse-engineering erodes proprietary edges.",[3817,3848,3849],{},"Blend approaches: Open standards (MCP, skill.md) enhance closed models; run open weights on your infra for flexibility.",[3817,3851,3852],{},"Build trust via transparency and controls, not secrecy—bad actors leak anyway; focus on implementation.",[3817,3854,3855],{},"For agents, treat all inputs as untrusted code—rethink textual data assumptions.",[3817,3857,3858],{},"Expect open models to trail but catch frontier capabilities quickly via collaborative innovation.",{"title":53,"searchDepth":54,"depth":54,"links":3860},[3861,3862,3863,3864,3865],{"id":3766,"depth":54,"text":3767},{"id":3776,"depth":54,"text":3777},{"id":3789,"depth":54,"text":3790},{"id":3802,"depth":54,"text":3803},{"id":3834,"depth":54,"text":3835},[61],{"content_references":3868,"triage":3878},[3869,3874],{"type":3870,"title":3871,"author":3872,"url":3873,"context":80},"podcast","Security Intelligence x Mixture of Experts Crossover Episode","IBM Technology (Matt Kazinski host)","https:\u002F\u002Fibm.biz\u002F~sTfk9xICA",{"type":3870,"title":3875,"author":3876,"url":3877,"context":71},"Mixture of Experts","IBM Technology","https:\u002F\u002Fibm.biz\u002F~SMOMF0sqx",{"relevance":3738,"novelty":3738,"quality":83,"actionability":54,"composite":3879,"reasoning":3880},3.05,"Category: AI & LLMs. The article discusses the role of open source in AI innovation and security, which aligns with the AI & LLMs category. While it presents some new perspectives on the security risks associated with open source AI, it lacks specific actionable steps for the audience to implement in their projects.","\u002Fsummaries\u002F9cf4eabf30c8f73e-open-source-ai-innovation-engine-or-security-risk-summary","2026-04-29 10:00:42","2026-05-03 16:43:49",{"title":3756,"description":53},{"loc":3881},"e15ac1bd93fe2101","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=PJGIWDW_W2A","summaries\u002F9cf4eabf30c8f73e-open-source-ai-innovation-engine-or-security-risk-summary",[101,99,98],"Panelists agree open source drives AI breakthroughs but warn it's 'securable' not 'secure'—needs rigorous practices to mitigate risks like model tampering and agent exploits.",[],"k2Bl5Kceueq5fD2_72bTo7FXkyKIWmwztjNZnjDyot8",{"id":3894,"title":3895,"ai":3896,"body":3901,"categories":3948,"created_at":62,"date_modified":62,"description":53,"extension":63,"faq":62,"featured":64,"kicker_label":62,"meta":3949,"navigation":86,"path":3957,"published_at":3958,"question":62,"scraped_at":3959,"seo":3960,"sitemap":3961,"source_id":3962,"source_name":3963,"source_type":94,"source_url":3964,"stem":3965,"tags":3966,"thumbnail_url":62,"tldr":3967,"tweet":62,"unknown_tags":3968,"__hash__":3969},"summaries\u002Fsummaries\u002Ffbbbc098d7e53ea7-hermes-agent-persists-learning-across-sessions-summary.md","Hermes Agent Persists Learning Across Sessions",{"provider":7,"model":8,"input_tokens":3897,"output_tokens":3898,"processing_time_ms":3899,"cost_usd":3900},3887,1356,12936,0.0014329,{"type":14,"value":3902,"toc":3943},[3903,3907,3910,3914,3922,3933,3936,3940],[17,3904,3906],{"id":3905},"session-amnesia-limits-current-ai-agents","Session Amnesia Limits Current AI Agents",[22,3908,3909],{},"Most AI agents today erase user-specific knowledge—like your tech stack, naming conventions, server details, or preferences—after each session. This forces repetitive context pasting, starting every conversation from scratch and wasting time on rediscovering basics. The result: agents feel like strangers, unable to build on prior help despite nodding along during interactions.",[17,3911,3913],{"id":3912},"hermes-builds-reusable-knowledge-via-learning-loop","Hermes Builds Reusable Knowledge via Learning Loop",[22,3915,3916,3917,3921],{},"Hermes Agent, an open-source project by Nous Research, embeds persistence from the ground up. Its core mechanism is a ",[3918,3919,3920],"em",{},"learning loop"," that:",[3814,3923,3924,3927,3930],{},[3817,3925,3926],{},"Records what worked in interactions.",[3817,3928,3929],{},"Distills those into reusable procedures.",[3817,3931,3932],{},"Automatically loads relevant procedures for matching future problems.",[22,3934,3935],{},"This isn't a bolted-on memory feature but a foundational design, turning one-off help into scalable, context-aware automation. Builders get an agent that evolves with use, reducing setup friction over time.",[17,3937,3939],{"id":3938},"practical-value-for-ai-agent-builders","Practical Value for AI Agent Builders",[22,3941,3942],{},"Hermes stands out among agents by addressing real-world retention gaps, making it ideal for ongoing workflows like coding or ops. Exploring its mechanics reveals patterns for your own agents: prioritize procedure extraction over raw chat history to enable true adaptation. If shipping persistent AI tools, benchmark against Hermes to avoid common forgetfulness pitfalls—it's a concrete step toward agents that compound value across sessions.",{"title":53,"searchDepth":54,"depth":54,"links":3944},[3945,3946,3947],{"id":3905,"depth":54,"text":3906},{"id":3912,"depth":54,"text":3913},{"id":3938,"depth":54,"text":3939},[61],{"content_references":3950,"triage":3955},[3951],{"type":68,"title":3952,"author":3953,"url":3954,"context":80},"Hermes Agent","Nous Research","https:\u002F\u002Fnousresearch.com\u002F",{"relevance":82,"novelty":83,"quality":83,"actionability":83,"composite":84,"reasoning":3956},"Category: AI & LLMs. The article discusses Hermes, an AI agent that learns across sessions, addressing a significant pain point for developers building AI tools. It provides actionable insights on how to implement a learning loop in AI agents, making it highly relevant and practical for the target audience.","\u002Fsummaries\u002Ffbbbc098d7e53ea7-hermes-agent-persists-learning-across-sessions-summary","2026-04-21 14:01:02","2026-04-21 15:26:08",{"title":3895,"description":53},{"loc":3957},"fbbbc098d7e53ea7","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fthe-ai-agent-that-actually-learns-from-you-inside-hermes-by-nous-research-fd074717a8e7?source=rss----98111c9905da---4","summaries\u002Ffbbbc098d7e53ea7-hermes-agent-persists-learning-across-sessions-summary",[98,99,101],"Unlike typical AI agents that reset context per session, Hermes from Nous Research uses a learning loop to capture successful procedures from interactions and auto-apply them to similar future tasks.",[],"lsDq90yZPxLIKlzGTbUAmJbeqmIwISt1vnbqR2XZ_TQ",{"id":3971,"title":3972,"ai":3973,"body":3978,"categories":4138,"created_at":62,"date_modified":62,"description":53,"extension":63,"faq":62,"featured":64,"kicker_label":62,"meta":4139,"navigation":86,"path":4165,"published_at":62,"question":62,"scraped_at":4166,"seo":4167,"sitemap":4168,"source_id":4169,"source_name":4170,"source_type":94,"source_url":4171,"stem":4172,"tags":4173,"thumbnail_url":62,"tldr":4174,"tweet":62,"unknown_tags":4175,"__hash__":4176},"summaries\u002Fsummaries\u002F7d871a9968ec8d6b-train-gpt-2-for-48-in-2-hours-on-8xh100-with-nanoc-summary.md","Train GPT-2 for $48 in 2 Hours on 8xH100 with nanochat",{"provider":7,"model":8,"input_tokens":3974,"output_tokens":3975,"processing_time_ms":3976,"cost_usd":3977},9517,2234,206553,0.001593,{"type":14,"value":3979,"toc":4132},[3980,3984,4004,4027,4031,4046,4050,4117,4121],[17,3981,3983],{"id":3982},"achieve-gpt-2-performance-at-fraction-of-original-cost","Achieve GPT-2 Performance at Fraction of Original Cost",[22,3985,3986,3987,3991,3992,3995,3996,3999,4000],{},"nanochat trains full GPT-2 equivalent models (1.6B params, CORE score 0.2565+) for $15-48 on spot\u002Fregular 8xH100 nodes (~$3\u002FGPU\u002Fhr, ~$24\u002Fhr\u002Fnode), versus GPT-2's 2019 $43k cost. Use single ",[3988,3989,3990],"code",{},"--depth"," dial (e.g., d24-d26 for GPT-2) to auto-set all hyperparameters: transformer width, heads, LR schedule, horizons, weight decay for compute-optimal scaling. Pretraining dominates compute; full pipeline (pretrain, SFT, RL, eval, inference, ChatGPT-like UI) runs end-to-end. Reproduce via ",[3988,3993,3994],{},"bash runs\u002Fspeedrun.sh"," on Lambda.ai 8xH100: ~2-3 hours to 4e19 FLOPs model. Serve with ",[3988,3997,3998],{},"python -m scripts.chat_web"," for web UI at http:\u002F\u002F",[4001,4002,4003],"public-ip",{},":8000. Model behaves like \"kindergartener\": hallucinates identity, explains sky color simply.",[22,4005,4006,4007,4010,4011,4014,4015,4018,4019,4022,4023,4026],{},"Trade-offs: Single GPU works (gradient accumulation, 8x slower); \u003C80GB VRAM needs ",[3988,4008,4009],{},"--device-batch-size"," reduction (32→16\u002F8\u002F4\u002F2\u002F1). CPU\u002FMPS via ",[3988,4012,4013],{},"runs\u002Fruncpu.sh"," (tiny model, weak results). Precision auto: bf16 on A100\u002FH100 (native tensor cores), fp32 on V100\u002FT4\u002FCPU\u002FMPS; override via ",[3988,4016,4017],{},"NANOCHAT_DTYPE=bfloat16\u002Ffloat16\u002Ffloat32",". Weights fp32 (optimizer), compute in ",[3988,4020,4021],{},"COMPUTE_DTYPE",", embeddings in reduced prec—no ",[3988,4024,4025],{},"torch.amp.autocast",".",[17,4028,4030],{"id":4029},"leaderboard-drives-community-optimization","Leaderboard Drives Community Optimization",[22,4032,4033,4034,4037,4038,4041,4042,4045],{},"\"Time-to-GPT-2\" leaderboard ranks wall-clock on 8xH100 to beat GPT-2 CORE 0.256525 via DCLM CORE eval (",[3988,4035,4036],{},"scripts.base_eval.py","). Current best: 1.65 hours (0.2626 CORE, ClimbMix dataset, autoresearch). Progress: 168hr (2019 GPT-2) → 3.04hr baseline → 2.91hr (fp8) → 2.76hr (1M token batch) → 2.02hr (ClimbMix) → 1.80hr (autoresearch r1) → 1.65hr (r2). Submit via ",[3988,4039,4040],{},"runs\u002Fspeedrun.sh","; see dev\u002FLEADERBOARD.md. Monitor wandb: val_bpb vs step\u002FFLOPs\u002Ftime, CORE, VRAM\u002FMFU\u002Ftok\u002Fsec. Quick expts: d12 (",[3988,4043,4044],{},"--depth=12",", ~5min pretrain) tests changes across depths.",[17,4047,4049],{"id":4048},"minimal-hackable-code-for-full-llm-pipeline","Minimal, Hackable Code for Full LLM Pipeline",[22,4051,4052,4053,4056,4057,4060,4061,4064,4065,4068,4069,4072,4073,4076,4077,4080,4081,4084,4085,4088,4089,4092,4093,4096,4097,4100,4101,4104,4105,4108,4109,4112,4113,4116],{},"~1k LoC PyTorch: ",[3988,4054,4055],{},"nanochat\u002Fgpt.py"," (transformer), ",[3988,4058,4059],{},"dataloader.py"," (distributed tokenizing), ",[3988,4062,4063],{},"optim.py"," (AdamW\u002FMuon), ",[3988,4066,4067],{},"tokenizer.py"," (BPE GPT-4 style), ",[3988,4070,4071],{},"engine.py"," (KV-cache inference), ",[3988,4074,4075],{},"execution.py"," (Python tool exec), ",[3988,4078,4079],{},"core_eval.py"," (DCLM CORE). Stages: ",[3988,4082,4083],{},"base_train.py"," (pretrain), ",[3988,4086,4087],{},"chat_sft.py"," (SFT), ",[3988,4090,4091],{},"chat_rl.py"," (RL), ",[3988,4094,4095],{},"chat_eval.py"," (tasks: ARC\u002FGSM8K\u002FMMLU\u002FHumanEval\u002Fspellingbee\u002FSmolTalk), ",[3988,4098,4099],{},"chat_cli\u002Fweb",". Tasks in ",[3988,4102,4103],{},"tasks\u002F",": mixtures\u002Fsequences. Data: FineWeb (HF), ClimbMix (NVIDIA). Setup: ",[3988,4106,4107],{},"uv sync --extra gpu --group dev"," (uv dep mgr). Scripts: ",[3988,4110,4111],{},"scaling_laws.sh","\u002F",[3988,4114,4115],{},"miniseries.sh"," sweep depths. No config monsters—depth drives all.",[17,4118,4120],{"id":4119},"research-and-customization-workflow","Research and Customization Workflow",[22,4122,4123,4124,4127,4128,4131],{},"Forkable baseline for \u003C$1k micro-models. Improve pretrain (e.g., dataset, fp8, batch=1M). Guides: Infuse personality via synthetic data (",[3988,4125,4126],{},"dev\u002Fgen_synthetic_data.py",") + SFT mix; add abilities (e.g., strawberry 'r' count) via tasks\u002Fcustomjson. Ex: ",[3988,4129,4130],{},"torchrun -m scripts.base_train --depth=12 --run=d12"," (wandb, no intermediates). PRs: Declare LLM contributions. Inspired by nanoGPT\u002Fmodded-nanoGPT. Cite as @misc{nanochat...}.",{"title":53,"searchDepth":54,"depth":54,"links":4133},[4134,4135,4136,4137],{"id":3982,"depth":54,"text":3983},{"id":4029,"depth":54,"text":4030},{"id":4048,"depth":54,"text":4049},{"id":4119,"depth":54,"text":4120},[61],{"content_references":4140,"triage":4162},[4141,4144,4147,4150,4153,4157,4159],{"type":68,"title":4142,"url":4143,"context":80},"uv","https:\u002F\u002Fdocs.astral.sh\u002Fuv\u002F",{"type":68,"title":4145,"url":4146,"context":80},"Lambda GPU Cloud","https:\u002F\u002Flambda.ai\u002Fservice\u002Fgpu-cloud",{"type":3734,"title":4148,"url":4149,"context":80},"nanoGPT","https:\u002F\u002Fgithub.com\u002Fkarpathy\u002FnanoGPT",{"type":3734,"title":4151,"url":4152,"context":80},"modded-nanoGPT","https:\u002F\u002Fgithub.com\u002FKellerJordan\u002Fmodded-nanogpt",{"type":4154,"title":4155,"author":4156,"context":80},"dataset","FineWeb","HuggingFace",{"type":4154,"title":4158,"author":4156,"context":80},"SmolTalk",{"type":68,"title":4160,"url":4161,"context":80},"DeepWiki","https:\u002F\u002Fdeepwiki.com\u002Fkarpathy\u002Fnanochat",{"relevance":82,"novelty":83,"quality":83,"actionability":82,"composite":4163,"reasoning":4164},4.55,"Category: AI & LLMs. The article provides a detailed guide on training GPT-2 models efficiently and cost-effectively, addressing the audience's need for practical applications in AI product development. It includes specific commands and parameters for implementation, making it immediately actionable.","\u002Fsummaries\u002F7d871a9968ec8d6b-train-gpt-2-for-48-in-2-hours-on-8xh100-with-nanoc-summary","2026-04-16 03:01:10",{"title":3972,"description":53},{"loc":4165},"7d871a9968ec8d6b","__oneoff__","https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fnanochat","summaries\u002F7d871a9968ec8d6b-train-gpt-2-for-48-in-2-hours-on-8xh100-with-nanoc-summary",[99,100,101],"nanochat trains GPT-2 capability LLMs (CORE score >0.2565) on a single 8xH100 GPU node for ~$48 (~2-3 hours wall-clock), with auto-optimal hyperparameters via single --depth dial, plus chat UI.",[],"ORueeVh-F7dwE9iVUlW3Gu6vJa1iZvPgawLZSA5XKkE"]