[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-41ef3a9324aac236-databricks-rag-low-dim-qwen3-rerank-for-89-recall-summary":3,"summaries-facets-categories":121,"summary-related-41ef3a9324aac236-databricks-rag-low-dim-qwen3-rerank-for-89-recall-summary":3706},{"id":4,"title":5,"ai":6,"body":13,"categories":79,"created_at":81,"date_modified":81,"description":73,"extension":82,"faq":81,"featured":83,"kicker_label":81,"meta":84,"navigation":102,"path":103,"published_at":104,"question":81,"scraped_at":105,"seo":106,"sitemap":107,"source_id":108,"source_name":109,"source_type":110,"source_url":111,"stem":112,"tags":113,"thumbnail_url":81,"tldr":118,"tweet":81,"unknown_tags":119,"__hash__":120},"summaries\u002Fsummaries\u002F41ef3a9324aac236-databricks-rag-low-dim-qwen3-rerank-for-89-recall--summary.md","Databricks RAG: Low-Dim Qwen3 + Rerank for 89% Recall@10",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6251,1773,31729,0.0021142,{"type":14,"value":15,"toc":72},"minimark",[16,21,25,29,61,65],[17,18,20],"h2",{"id":19},"minimize-dimensions-and-tune-queries-to-cut-latency-without-losing-recall","Minimize Dimensions and Tune Queries to Cut Latency Without Losing Recall",[22,23,24],"p",{},"Higher-dim embeddings (1024-1536) increase ANN scan costs, memory use, and slow throughput—test empirically to pick the lowest dim preserving recall@10, like 384 over 1024 if equivalent. Limit num_results to 10-100 (default 50 with reranker, 10 without) since HNSW scales linearly and excess slows queries without better answers. Match endpoint SKU to scale: Standard for \u003C2M 768-dim vectors (low latency), Storage-Optimized for \u003C1B vectors (cheaper, higher latency, dims divisible by 16, triggered sync only). Add metadata filters (e.g., {\"document_type\": \"manual\"}) to Delta tables for scoped ANN scans, boosting precision\u002Fspeed. Stick to ANN for semantic queries (highest QPS); hybrid (ANN+BM25) only for exact terms like SKUs or ISO 13849-1.",[17,26,28],{"id":27},"self-manage-qwen3-mrl-embeddings-to-hit-target-dims-like-256","Self-Manage Qwen3 MRL Embeddings to Hit Target Dims Like 256",[22,30,31,32,36,37,40,41,44,45,48,49,52,53,56,57,60],{},"Fixed-dim models like databricks-gte-large-en (always 1024) force re-embedding for size changes. Qwen3-Embedding-0.6B uses Matryoshka Representation Learning (MRL) to pack signal into early dims, enabling safe truncation to any power-of-2 (32-1024). Managed Delta sync ignores ",[33,34,35],"code",{},"dimensions"," param, always outputs 1024—use self-managed: pre-compute with API (",[33,38,39],{},"{\"input\": [text], \"dimensions\": 256}","), UDF to Delta table (",[33,42,43],{},"chunk_embedding","), then index with ",[33,46,47],{},"embedding_vector_column"," and ",[33,50,51],{},"embedding_dimension=256",". Query same way: embed query at 256, pass vector to ",[33,54,55],{},"similarity_search",". For prod scale, swap UDF for ",[33,58,59],{},"ai_query()"," batch inference.",[17,62,64],{"id":63},"rerank-top-50-ann-hits-for-15pt-recall-gain-over-vector-distance-alone","Rerank Top-50 ANN Hits for 15pt Recall Gain Over Vector Distance Alone",[22,66,67,68,71],{},"ANN cosine similarity doesn't guarantee query relevance—close vectors (e.g., \"sensor calibration\" vs. \"actuator recalibration\") rank by distance, not utility. Databricks Reranker re-scores top-50 with query-aware model: 74% ANN-only recall@10 jumps to 89% (+15pts), beating cloud rivals by 10pts. Enable via ",[33,69,70],{},"reranker={\"model\": \"databricks_reranker\", \"parameters\": {\"columns_to_rerank\": [\"chunk\", \"doc_summary\"]}}"," (first 2000 chars, richest first; order matters). Adds ~1.5s latency—skip only for \u003C200ms needs, >5 QPS unscaled, or non-RAG search. Production stack: Qwen3@256dims (self-managed), ANN HNSW, triggered Delta sync, rerank metadata.",{"title":73,"searchDepth":74,"depth":74,"links":75},"",2,[76,77,78],{"id":19,"depth":74,"text":20},{"id":27,"depth":74,"text":28},{"id":63,"depth":74,"text":64},[80],"AI & LLMs",null,"md",false,{"content_references":85,"triage":97},[86,90,92,95],{"type":87,"title":88,"context":89},"tool","databricks-qwen3-embedding-0-6b","recommended",{"type":87,"title":91,"context":89},"databricks_reranker",{"type":87,"title":93,"context":94},"databricks-gte-large-en","mentioned",{"type":87,"title":96,"context":94},"databricks-bge-large-en",{"relevance":98,"novelty":99,"quality":99,"actionability":98,"composite":100,"reasoning":101},5,4,4.55,"Category: AI & LLMs. The article provides practical insights on optimizing embedding dimensions and reranking techniques for improved recall in AI applications, addressing specific pain points for developers integrating AI features. It includes actionable steps for implementation, such as using self-managed embeddings and reranking methods, making it highly relevant and actionable for the target audience.",true,"\u002Fsummaries\u002F41ef3a9324aac236-databricks-rag-low-dim-qwen3-rerank-for-89-recall-summary","2026-05-05 05:52:27","2026-05-05 16:09:29",{"title":5,"description":73},{"loc":103},"41ef3a9324aac236","Towards AI","article","https:\u002F\u002Fpub.towardsai.net\u002Fvector-search-done-right-best-practices-qwen3-dimension-control-and-why-reranking-is-e021e18be13c?source=rss----98111c9905da---4","summaries\u002F41ef3a9324aac236-databricks-rag-low-dim-qwen3-rerank-for-89-recall--summary",[114,115,116,117],"python","machine-learning","ai-llms","ai-automation","Minimize embedding dims to 256 with Qwen3 MRL (self-managed path), set num_results=50, always rerank ANN top-50 candidates for +15pts recall@10 over 74% 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Framework: Deterministic AI Safety via Geometric Constraints",{"provider":7,"model":8,"input_tokens":3711,"output_tokens":3712,"processing_time_ms":3713,"cost_usd":3714},5527,2073,10155,0.00163505,{"type":14,"value":3716,"toc":3750},[3717,3721,3724,3727,3731,3734,3737,3741,3744,3747],[17,3718,3720],{"id":3719},"three-layer-boundary-for-proactive-ai-governance","Three-Layer Boundary for Proactive AI Governance",[22,3722,3723],{},"Build deterministic AI safety by structuring operations into H2E's World Model Layer (V-JEPA 2's self-supervised spatiotemporal video embeddings for real-time ground truth), Geometric Governance (prevents unsafe outputs by hardcoding constraints into model logic\u002Fweights, making violations mathematically impossible), and Deterministic Reasoning (requires verifiable claims before token generation). This shifts from probabilistic guessing to expert kernels tied to physical reality, enabling Sovereign AI with auditable local hosting under SAIL licenses—AI executes only if aligned, treating the 'Wall' (geometric bounds) as a technical-legal contract that breaches on deviation.",[22,3725,3726],{},"Trade-off: Sacrifices flexibility of cloud models for mission-critical determinism in aerospace\u002Fgovernment, avoiding foreign update risks while ensuring outputs match expert protocols.",[17,3728,3730],{"id":3729},"perception-action-loop-grounds-reasoning-in-video-data","Perception-Action Loop Grounds Reasoning in Video Data",[22,3732,3733],{},"Process raw video into safe actions: Sample 16 frames (256x256) via PyAV, extract 1024D visual embeddings with V-JEPA 2 (Hugging Face transformers, vjepa2-vitl-fpc64-256), select 4 keyframes for Claude 4.7 API (prompted as 'expert aviation safety controller' for tasks like landing gear failure). Claude analyzes pixels directly for ACTION\u002FEXPLANATION (e.g., low fly-by inspection, runway clearance, ARFF positioning), projecting visual embedding to 384D text space via linear layer for multimodal fusion.",[22,3735,3736],{},"Outcome: Ties reasoning to observable reality, preventing hallucinations—initial Claude output on gear failure video recommends protocol steps verifiable against visuals.",[17,3738,3740],{"id":3739},"sroi-verification-and-nested-adaptation-enforce-alignment","SROI Verification and Nested Adaptation Enforce Alignment",[22,3742,3743],{},"Compute Semantic Return-of-Investment (SROI) as cosine similarity between AI outputs and Expert Intents library: Visual SROI (embedding vs. intents), Text SROI (Claude text vs. intents), Fused SROI average. Reject if \u003C0.75 threshold (e.g., initial 0.0362 visual + 0.5802 text = 0.3082 fused flags 'Representation Gap', blocks action).",[22,3745,3746],{},"Trigger Nested Learning: Freeze V-JEPA\u002FClaude backbones, Adam-optimize projector weights over 100 steps (loss drops 0.0420 to 0.0000, Fused SROI rises to 0.7901). Authorizes aligned action only post-convergence, logging full transparency from pixels to verified decision.",[22,3748,3749],{},"Impact: Adapts without retraining giants, ensuring 100% protocol compliance in high-stakes loops—transforms probability-based AI into deterministic expert systems for aviation safety.",{"title":73,"searchDepth":74,"depth":74,"links":3751},[3752,3753,3754],{"id":3719,"depth":74,"text":3720},{"id":3729,"depth":74,"text":3730},{"id":3739,"depth":74,"text":3740},[],{"content_references":3757,"triage":3766},[3758,3763],{"type":3759,"title":3760,"url":3761,"context":3762},"other","The Wall Before the Word: H2E Geometric Governance and the Future of AI Government","https:\u002F\u002Fmedium.com\u002Fai-simplified-in-plain-english\u002Fthe-wall-before-the-word-h2e-geometric-governance-and-the-future-of-ai-government-89ff82c7598a","cited",{"type":87,"title":3764,"url":3765,"context":94},"h2e_vjepa2_claude4dot7.ipynb","https:\u002F\u002Fgithub.com\u002Ffrank-morales2020\u002FMLxDL\u002Fblob\u002Fmain\u002Fh2e_vjepa2_claude4dot7.ipynb",{"relevance":3767,"novelty":99,"quality":99,"actionability":74,"composite":3768,"reasoning":3769},3,3.25,"Category: AI & LLMs. The article discusses a framework for deterministic AI safety, which is relevant to AI engineering and addresses the need for practical applications in AI product development. However, while it presents novel insights into AI safety mechanisms, it lacks sufficient actionable steps for the audience to implement the concepts discussed.","\u002Fsummaries\u002F3fc7b2368b61c268-h2e-framework-deterministic-ai-safety-via-geometri-summary","2026-04-16 19:38:58","2026-04-19 01:22:22",{"title":3709,"description":73},{"loc":3770},"3fc7b2368b61c268","AI Simplified in Plain English","https:\u002F\u002Fmedium.com\u002Fai-simplified-in-plain-english\u002Fdeterministic-alignment-the-h2e-framework-v-jepa-2-claude-4-7-ae8b61fa8b8b?source=rss----f37ab7d4e76b---4","summaries\u002F3fc7b2368b61c268-h2e-framework-deterministic-ai-safety-via-geometri-summary",[114,3780,116,117],"prompt-engineering","Embed safety as mathematical impossibilities in AI via H2E's three layers: V-JEPA 2 grounds video perception in 1024D reality embeddings, Claude 4.7 reasons multimodally, SROI verifies fused alignment >0.75 threshold or adapts projector weights over 100 steps to ensure expert-compliant actions in aviation.",[116,117],"5BOOkb9r4C0i1fDATFdg8iwfche3b71djRSeJMweX6U",{"id":3785,"title":3786,"ai":3787,"body":3792,"categories":3885,"created_at":81,"date_modified":81,"description":3886,"extension":82,"faq":81,"featured":83,"kicker_label":81,"meta":3887,"navigation":102,"path":3888,"published_at":3889,"question":81,"scraped_at":3890,"seo":3891,"sitemap":3892,"source_id":3893,"source_name":3894,"source_type":3895,"source_url":3896,"stem":3897,"tags":3898,"thumbnail_url":81,"tldr":3900,"tweet":81,"unknown_tags":3901,"__hash__":3902},"summaries\u002Fsummaries\u002Fc26381c52d1b03a3-claude-code-loops-generate-100-200-week-passive-in-summary.md","Claude Code Loops Generate $100-200\u002FWeek Passive Income",{"provider":7,"model":8,"input_tokens":3788,"output_tokens":3789,"processing_time_ms":3790,"cost_usd":3791},6586,1611,17519,0.00210005,{"type":14,"value":3793,"toc":3879},[3794,3798,3822,3826,3848,3852,3862,3866],[17,3795,3797],{"id":3796},"build-reusable-claude-skills-for-step-by-step-automation","Build Reusable Claude Skills for Step-by-Step Automation",[22,3799,3800,3801,3804,3805,3809,3810,3813,3814,3817,3818,3821],{},"Claude skills are .md files defining structured, repeatable tasks via numbered steps, invoked with ",[33,3802,3803],{},"claude-p \u002Fskillname"," in terminal for headless execution. To create one, prompt Claude Code: \"fetch Anthropic Claude Code skills docs; create placeholder for ",[3806,3807,3808],"span",{},"skillname",".md\". Edit the template to outline exact steps—e.g., for bug hunting: (1) poll Kali for new prediction markets and log to ",[33,3811,3812],{},"new_markets.json","; (2) group events by type; (3) run checklist for vulnerabilities (frontend batches, logs, exploits); (4) if bug found, email support@kali with details. Integrate Python scripts for data fetching (",[33,3815,3816],{},"fetch_new_markets.py","), JSON processing, and emailing via Gmail token.json. This modular setup runs reliably every cycle, avoiding ad-hoc prompts. Claude handles third-party API changes better than wrappers like OpenClaude, as ",[33,3819,3820],{},"\u002Fskill"," triggers official execution.",[17,3823,3825],{"id":3824},"infinite-bash-loop-enables-247-hands-off-operation","Infinite Bash Loop Enables 24\u002F7 Hands-Off Operation",[22,3827,3828,3829,3832,3833,3836,3837,3839,3840,3843,3844,3847],{},"Wrap skill invocation in ",[33,3830,3831],{},"while true; do claude-p \u002Fskillname; sleep 60; done"," to loop indefinitely, pausing 60 seconds (or 3600 for hourly) post-run. Run in a separate terminal outside Claude Code session. Update ",[33,3834,3835],{},"settings.json"," to auto-approve bash commands: add permissions for ",[33,3838,3816],{},", ",[33,3841,3842],{},"send_report.py",". This yields true passivity—e.g., one loop scans Kali markets continuously, detecting minor bugs ($25 bounty), moderate ($50), severe ($100), or pre-listing extras ($10). Author nets $100-200\u002Fweek from this alone; others yield $20-300 or losses (unshared). Scale by adjusting sleep for API limits, adding system prompts via ",[33,3845,3846],{},"claude-p --system",", or chaining bash commands. GitHub repo (AllAboutAI-YT) provides open templates.",[17,3849,3851],{"id":3850},"profitable-example-kali-bug-bounty-scanner","Profitable Example: Kali Bug Bounty Scanner",[22,3853,3854,3855,3858,3859,3861],{},"Target Kali's market bug bounty: poll for new prediction markets, extract logs via ",[33,3856,3857],{},"claim_log",", analyze for exploits using fixed checklist (ensures consistency). On match, auto-email support@kali with proof. Pair watcher script logging to ",[33,3860,3812],{}," with skill consuming it—loop triggers only on fresh data, minimizing noise. Outcomes: fully autonomous, low-creativity barrier (Claude generates 80% code), runs headless. Trade-offs: rare severe bugs; on\u002Foff earnings from market volume. Replicate for any bounty\u002FAPI monitoring—author runs multiples, some breakeven.",[17,3863,3865],{"id":3864},"quick-demo-hacker-news-email-digest","Quick Demo: Hacker News Email Digest",[22,3867,3868,3869,3872,3873,3875,3876,3878],{},"Prompt Claude: \"Automail skill: step-by-step fetch top 5 Hacker News posts (URLs, scores), save news.json, send Gmail via token.json.\" Steps: (1) ",[33,3870,3871],{},"python fetch_hn.py"," for JSON; (2) ",[33,3874,3842],{}," emails digest. Loop sends every 60s (demo flaw: duplicates; fix with hourly sleep). Permissions fix: ",[33,3877,3835],{}," allows scripts. Result: 5 emails with titles like \"Built camera-only vacuum roll for \u003C$300\" link to HN. Refine to dedupe or schedule for production—proves loop scales to income tasks.",{"title":73,"searchDepth":74,"depth":74,"links":3880},[3881,3882,3883,3884],{"id":3796,"depth":74,"text":3797},{"id":3824,"depth":74,"text":3825},{"id":3850,"depth":74,"text":3851},{"id":3864,"depth":74,"text":3865},[132],"My Easy Claude Code Passive Income AI Automation Setup\n\n👊 Become a YouTube Member to Support Me:\nhttps:\u002F\u002Fwww.youtube.com\u002Fc\u002FAllAboutAI\u002Fjoin\n\nFor Agents:\nwww.skillsmd.store\n\nMy AI Video Course:\nhttps:\u002F\u002Fwww.theaivideocourse.com\u002F\n\n🔥Open GH:\nhttps:\u002F\u002Fgithub.com\u002FAllAboutAI-YT\u002F\n\nBusiness Inquiries:\nkbfseo@gmail.com​",{},"\u002Fsummaries\u002Fc26381c52d1b03a3-claude-code-loops-generate-100-200-week-passive-in-summary","2026-04-08 11:33:12","2026-04-08 14:47:47",{"title":3786,"description":3886},{"loc":3888},"c26381c52d1b03a3","All About AI","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3hioz8dlTFs","summaries\u002Fc26381c52d1b03a3-claude-code-loops-generate-100-200-week-passive-in-summary",[3899,114,117,116],"automation","Run Claude skills in a bash 'while true' loop with 'sleep 60' to automate tasks 24\u002F7: scan Kali markets for bugs worth $25-100 each and auto-email reports, or send Hacker News digests.",[117,116],"0ZLh7qHbbHaUipbRBSFAtzccOV_tZEavl9WbqtYsxOw",{"id":3904,"title":3905,"ai":3906,"body":3911,"categories":3939,"created_at":81,"date_modified":81,"description":73,"extension":82,"faq":81,"featured":83,"kicker_label":81,"meta":3940,"navigation":102,"path":3951,"published_at":3952,"question":81,"scraped_at":3953,"seo":3954,"sitemap":3955,"source_id":3956,"source_name":109,"source_type":110,"source_url":3957,"stem":3958,"tags":3959,"thumbnail_url":81,"tldr":3960,"tweet":81,"unknown_tags":3961,"__hash__":3962},"summaries\u002Fsummaries\u002F092f953f13e749e1-reproduce-2011-sentiment-word-vectors-in-python-summary.md","Reproduce 2011 Sentiment Word Vectors in Python",{"provider":7,"model":8,"input_tokens":3907,"output_tokens":3908,"processing_time_ms":3909,"cost_usd":3910},3933,1516,16200,0.00152195,{"type":14,"value":3912,"toc":3934},[3913,3917,3920,3924,3927,3931],[17,3914,3916],{"id":3915},"elegant-core-technique-semantic-learning-from-ratings","Elegant Core Technique: Semantic Learning from Ratings",[22,3918,3919],{},"Maas et al. (2011) train sentiment-specific word vectors directly from unlabeled IMDb movie reviews paired with star ratings (1-10 scale). Words co-occurring in high-rated reviews pull closer in vector space; low-rated push apart. This creates representations capturing sentiment polarity without explicit labels. Final classification uses linear SVM on averaged review vectors, achieving strong accuracy through interpretable, low-dimensional embeddings. Author notes its logistic regression-like simplicity: powerful when data aligns with task, avoiding black-box complexity.",[17,3921,3923],{"id":3922},"reproduction-insights-and-modern-relevance","Reproduction Insights and Modern Relevance",[22,3925,3926],{},"Reproducing the paper in Python reveals its enduring strength – elegant semantic learning outperforms hype-driven alternatives in targeted tasks like sentiment. Author challenges original methods, tests against other representations (including LLMs), and automates full pipeline. Trade-off: excels on review-style text but needs domain data; not general-purpose like transformers. GitHub repo provides end-to-end code for immediate use or extension.",[17,3928,3930],{"id":3929},"practical-takeaways-for-builders","Practical Takeaways for Builders",[22,3932,3933],{},"Start with this for sentiment features in products: download IMDb data, train vectors via contrastive objective on ratings, classify with SVM. Scales to custom corpora (e.g., product feedback). Compares favorably to LLMs on cost\u002Finterpretability; use as baseline before deploying APIs. Avoids overfitting by leveraging vast unlabeled text – key for production ML pipelines.",{"title":73,"searchDepth":74,"depth":74,"links":3935},[3936,3937,3938],{"id":3915,"depth":74,"text":3916},{"id":3922,"depth":74,"text":3923},{"id":3929,"depth":74,"text":3930},[174],{"content_references":3941,"triage":3949},[3942,3946],{"type":3943,"title":3944,"author":3945,"context":94},"paper","Learning Word Vectors for Sentiment Analysis","Maas et al.",{"type":3759,"title":3947,"url":3948,"context":94},"Sentiment_analysis","https:\u002F\u002Fgithub.com\u002FJumbong\u002FSentiment_analysis",{"relevance":98,"novelty":99,"quality":99,"actionability":98,"composite":100,"reasoning":3950},"Category: AI & LLMs. The article provides a practical method for building sentiment-aware word embeddings, which is directly applicable for product builders looking to integrate sentiment analysis into their AI-powered products. It includes actionable steps and a GitHub repository for implementation, making it highly relevant and actionable.","\u002Fsummaries\u002F092f953f13e749e1-reproduce-2011-sentiment-word-vectors-in-python-summary","2026-05-10 00:01:00","2026-05-10 15:26:28",{"title":3905,"description":73},{"loc":3951},"092f953f13e749e1","https:\u002F\u002Fpub.towardsai.net\u002Flearning-word-vectors-for-sentiment-analysis-a-python-reproduction-f8c8c77df38f?source=rss----98111c9905da---4","summaries\u002F092f953f13e749e1-reproduce-2011-sentiment-word-vectors-in-python-summary",[114,115],"Build sentiment-aware word embeddings from IMDb reviews via semantic learning with star ratings and linear SVM classification, reproducing Maas et al. (2011) – simple method rivals modern LLMs.",[],"v2XTBE5rFNMZcIts4tjxKmc0d5a3j51Waw-d4ggTQcI",{"id":3964,"title":3965,"ai":3966,"body":3971,"categories":4109,"created_at":81,"date_modified":81,"description":73,"extension":82,"faq":81,"featured":83,"kicker_label":81,"meta":4110,"navigation":102,"path":4111,"published_at":4112,"question":81,"scraped_at":81,"seo":4113,"sitemap":4114,"source_id":4115,"source_name":4116,"source_type":110,"source_url":4117,"stem":4118,"tags":4119,"thumbnail_url":81,"tldr":4120,"tweet":81,"unknown_tags":4121,"__hash__":4122},"summaries\u002Fsummaries\u002Fnes-optimizes-quadratic-bowl-via-gaussian-perturba-summary.md","NES optimizes quadratic bowl via gaussian perturbations",{"provider":7,"model":8,"input_tokens":3967,"output_tokens":3968,"processing_time_ms":3969,"cost_usd":3970},8855,1292,10281,0.0019466,{"type":14,"value":3972,"toc":4104},[3973,3977,3980,4017,4028,4041,4044,4048,4051,4066,4069,4073,4100],[17,3974,3976],{"id":3975},"nes-core-loop-for-black-box-optimization","NES Core Loop for Black-Box Optimization",[22,3978,3979],{},"NES treats parameters w as mean of a fixed-variance gaussian (sigma=0.1). To maximize black-box reward f(w) without gradients:",[3981,3982,3983,3987,4007,4010],"ol",{},[3984,3985,3986],"li",{},"Generate npop=50 noise samples N ~ N(0,1) (shape 50x3).",[3984,3988,3989,3990,3993,3994,3996,3997,3999,4000,4002,4003,4006],{},"Perturb: w_try",[3806,3991,3992],{},"j"," = w + sigma * N",[3806,3995,3992],{},", compute R",[3806,3998,3992],{}," = f(w_try",[3806,4001,3992],{},"). Here f(w) = -||w - ",[3806,4004,4005],{},"0.5,0.1,-0.3","||^2_2 (max reward=0 at solution).",[3984,4008,4009],{},"Standardize: A = (R - mean(R)) \u002F std(R) to zero-mean unit-variance (avoids div-by-zero on flat rewards; speeds convergence vs raw R).",[3984,4011,4012,4013,4016],{},"Update: w += alpha\u002F(npop * sigma) * N.T @ A (alpha=0.001). This is score-function gradient estimator E",[3806,4014,4015],{},"reward * noise","\u002Fsigma.",[22,4018,4019,4020,4023,4024,4027],{},"Starts from random w≈",[3806,4021,4022],{},"1.76,0.40,0.98"," (reward -3.32), reaches ",[3806,4025,4026],{},"-0.000009"," error by iter 280.",[4029,4030,4033],"pre",{"className":4031,"code":4032,"language":114,"meta":73,"style":73},"language-python shiki shiki-themes github-light github-dark","w = w + alpha\u002F(npop*sigma) * np.dot(N.T, A)\n",[33,4034,4035],{"__ignoreMap":73},[3806,4036,4039],{"class":4037,"line":4038},"line",1,[3806,4040,4032],{},[22,4042,4043],{},"sigma scales perturbation size and normalizes estimator (divisor matches multiplier for consistent gradient scale).",[17,4045,4047],{"id":4046},"proven-convergence-on-toy-quadratic","Proven Convergence on Toy Quadratic",[22,4049,4050],{},"300 iters suffice; prints every 20 show steady progress:",[4052,4053,4054,4057,4060,4063],"ul",{},[3984,4055,4056],{},"Iter 0: reward -3.323",[3984,4058,4059],{},"Iter 100: -0.727",[3984,4061,4062],{},"Iter 200: -0.001",[3984,4064,4065],{},"Iter 280: -0.000009",[22,4067,4068],{},"Toy mimics NN optimization: f(w) would forward NN on env, return total reward. Solution hidden from optimizer.",[17,4070,4072],{"id":4071},"insights-from-implementers","Insights from Implementers",[4052,4074,4075,4082,4088,4094],{},[3984,4076,4077,4081],{},[4078,4079,4080],"strong",{},"Standardization optional but boosts speed",": Raw R works (paper-equivalent via Section 3.2), but centering\u002Fscaling prevents stagnation on negative\u002Fflat rewards.",[3984,4083,4084,4087],{},[4078,4085,4086],{},"Edge cases",": Add epsilon to std(R) avoids div0 when all R equal (common early\u002Fsimple problems).",[3984,4089,4090,4093],{},[4078,4091,4092],{},"Extensions",": Handles moving targets with small jitters; libs like evostra apply to Flappy Bird. No crossover needed vs GA—NES is gradient-like via log-prob derivative.",[3984,4095,4096,4099],{},[4078,4097,4098],{},"Deployment",": Save final w; reconstruct NN. Practical for RL vs DQN (no backprop, parallelizable evals).",[4101,4102,4103],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":73,"searchDepth":74,"depth":74,"links":4105},[4106,4107,4108],{"id":3975,"depth":74,"text":3976},{"id":4046,"depth":74,"text":4047},{"id":4071,"depth":74,"text":4072},[174],{},"\u002Fsummaries\u002Fnes-optimizes-quadratic-bowl-via-gaussian-perturba-summary","2026-04-08 21:21:20",{"title":3965,"description":73},{"loc":4111},"24c62cc73ee60bc6","Andrej Karpathy Gists","https:\u002F\u002Funknown","summaries\u002Fnes-optimizes-quadratic-bowl-via-gaussian-perturba-summary",[114,115],"Sample 50 perturbed weights from N(w, 0.1), weight by standardized rewards, update w by 0.001\u002F(50*0.1) * sum(noise * weights) to converge in 300 iters.",[],"THgP6_hPLQzW9Arl2BqfDCHYij8HS6-ncC3XkmeXu-Y"]