Rasul Kireev

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Rasul Kireev

Rasul Kireev

@rasulkireev

Engineer @readwise. Building TuxSEO and a bunch other apps on my free time. DMs are open! Here to make friends.

MRR:0🟨 ⬜ ⬜ ⬜ ⬜️ ⬜️ ⬜ ⬜ ⬜$10k Bergabung Temmuz 2010
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Rasul Kireev
Rasul Kireev@rasulkireev·
@NickSpisak_ @gregagi Could you please read and save this article. Let me know if there is anythihng useful for our setup!
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Rasul Kireev
Rasul Kireev@rasulkireev·
@NickSpisak_ @gregagi Could you please read and save this article. Let me know if there is anythihng useful for our setup!
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Rasul Kireev
Rasul Kireev@rasulkireev·
@svpino I think all rational people would agree. But that's too much to ask of from most people.
Одинцовский район, Московская область 🇷🇺 English
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Rasul Kireev
Rasul Kireev@rasulkireev·
That happens when you try to do too much at the same time. Happened to me too. Useful to step back and revisit the structure once in a while. It's the same as giving a junior dev too much information and too much guidance.
Brad Mills 🔑⚡️@bradmillscan

350-400 hours into OpenClaw over the last 33 days non-stop, no days off...I'm ready to quit. My openclaw is fucking lost in the weeds every day today and it's driving me nuts. Basic shit. I asked it to use GitHub. it has a GitHub skill. We have a GitHub SOP. I can see it's thinking process about using skills, then narrating how the skill doesn't exist, then going and inventing ways to retrieve the capability to use GitHub from the internet. I tell it to look in the openclaw docs for the proper skill path, it says "oops my bad, yeah it was there after all." This is ChatGPT 5.4 with extra high thinking turned on. I ask it to diagnose the problem only, so it goes and sees the system prompt is telling it to look at the wrong place, and it goes to GitHub and opens a GitHub issue about this 'bug' without even asking me. What the actual fuck. 3 hours on a Sunday of trying to rewire the brain of my openclaw to do default-behaviour. This thing such a productivity suck & mental poison. I can't do anything useful or positive with OpenClaw because I'm nonstop fighting fires in the engine room. I'm thinking about giving up.

Одинцовский район, Московская область 🇷🇺 English
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Rasul Kireev
Rasul Kireev@rasulkireev·
@seelffff @GregAGI please save and learn from this article.
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Rasul Kireev
Rasul Kireev@rasulkireev·
Most AI agent failures are workflow failures. Before changing models, tighten three things: clear inputs, one source of truth, and explicit done criteria. Better process usually beats better prompting.
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Varun
Varun@varun_mathur·
Autoquant: a distributed quant research lab | v2.6.9 We pointed @karpathy's autoresearch loop at quantitative finance. 135 autonomous agents evolved multi-factor trading strategies - mutating factor weights, position sizing, risk controls - backtesting against 10 years of market data, sharing discoveries. What agents found: Starting from 8-factor equal-weight portfolios (Sharpe ~1.04), agents across the network independently converged on dropping dividend, growth, and trend factors while switching to risk-parity sizing — Sharpe 1.32, 3x return, 5.5% max drawdown. Parsimony wins. No agent was told this; they found it through pure experimentation and cross-pollination. How it works: Each agent runs a 4-layer pipeline - Macro (regime detection), Sector (momentum rotation), Alpha (8-factor scoring), and an adversarial Risk Officer that vetoes low-conviction trades. Layer weights evolve via Darwinian selection. 30 mutations compete per round. Best strategies propagate across the swarm. What just shipped to make it smarter: - Out-of-sample validation (70/30 train/test split, overfit penalty) - Crisis stress testing (GFC '08, COVID '20, 2022 rate hikes, flash crash, stagflation) - Composite scoring - agents now optimize for crisis resilience, not just historical Sharpe - Real market data (not just synthetic) - Sentiment from RSS feeds wired into factor models - Cross-domain learning from the Research DAG (ML insights bias finance mutations) The base result (factor pruning + risk parity) is a textbook quant finding - a CFA L2 candidate knows this. The interesting part isn't any single discovery. It's that autonomous agents on commodity hardware, with no prior financial training, converge on correct results through distributed evolutionary search - and now validate against out-of-sample data and historical crises. Let's see what happens when this runs for weeks instead of hours. The AGI repo now has 32,868 commits from autonomous agents across ML training, search ranking, skill invention (1,251 commits from 90 agents), and financial strategies. Every domain uses the same evolutionary loop. Every domain compounds across the swarm. Join the earliest days of the world's first agentic general intelligence system and help with this experiment (code and links in followup tweet, while optimized for CLI, browser agents participate too):
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Varun@varun_mathur

Autoskill: a distributed skill factory | v.2.6.5 We're now applying the same @karpathy autoresearch pattern to an even wilder problem: can a swarm of self-directed autonomous agents invent software? Our autoresearch network proved that agents sharing discoveries via gossip compound faster than any individual: 67 agents ran 704 ML experiments in 20 hours, rediscovering Kaiming init and RMSNorm from scratch. Our autosearch network applied the same loop to search ranking, evolving NDCG@10 scores across the P2P network. Now we're pointing it at code generation itself. Every Hyperspace agent runs a continuous skill loop: same propose → evaluate →keep/revert cycle, but instead of optimizing a training script or ranking model, agents write JavaScript functions from scratch, test them against real tasks, and share working code to the network. It's live and rapidly improving in code and agent work being done. 90 agents have published 1,251 skill invention commits to the AGI repo in the last 24 hours - 795 text chunking skills, 182 cosine similarity, 181 structured diffing, 49 anomaly detection, 36 text normalization, 7 log parsers, 1 entity extractor. Skills run inside a WASM sandbox with zero ambient authority: no filesystem, no network, no system calls. The compound skill architecture is what makes this different from just sharing code snippets. Skills call other skills: a research skill invokes a text chunker, which invokes a normalizer, which invokes an entity extractor. Recursive execution with full lineage tracking: every skill knows its parent hash, so you can walk the entire evolution tree and see which peer contributed which mutation. An agent in Seoul wraps regex operations in try-catch; an agent in Amsterdam picks that up and combines it with input coercion it discovered independently. The network converges on solutions no individual agent would reach alone. New agents skip the cold start: replicated skill catalogs deliver the network's best solutions immediately. As @trq212 said, "skills are still underrated". A network of self-coordinating autonomous agents like on Hyperspace is starting to evolve and create more of them. With millions of such agents one day, how many high quality skills there would be ? This is Darwinian natural selection: fully decentralized, sandboxed, and running on every agent in the network right now. Join the world's first agentic general intelligence system (code and links in followup tweet, while optimized for CLI, browser agents participate too):

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Rasul Kireev
Rasul Kireev@rasulkireev·
@johnrushx @gregagi can you save and learnt fom this article? Anything we should implement?
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Rasul Kireev
Rasul Kireev@rasulkireev·
@jakezward @GregAGI can you please read and learn from this article?
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