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@akshay_pachaar

  • Claude.md File Reaches 15K Stars with AI Coding Guidelines
    Claude.md File Reaches 15K Stars with AI Coding Guidelines

    By

    @akshay_pachaar

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    A single ๐—–๐—Ÿ๐—”๐—จ๐——๐—˜.๐—บ๐—ฑ file just hit 15K GitHub stars. (derived from Karpathy's coding rules) Andrej Karpathy observed that LLMs make the same predictable mistakes when writing code: over-engineering, ignoring existing patterns, and adding dependencies you never asked for. If you've used AI coding assistants, you've hit all of these. But here's the thing: If the mistakes are predictable, you can prevent them with the right instructions. That's exactly what this ๐—–๐—Ÿ๐—”๐—จ๐——๐—˜.๐—บ๐—ฑ does. You drop one markdown file into your repo, and it gives Claude Code a structured set of behavioral guidelines for your entire project. This is a big deal. – Built entirely around prompt engineering for AI coding assistants – No framework, no complex tooling, just one .md file that shapes behavior Developers are moving past "use AI to write code" and into "engineer the AI's behavior so the code is actually good." The Claude Code ecosystem is growing fast, and the best tools in it aren't always software. Sometimes they're just well-crafted instructions. 100% open-source. I've shared a link to the GitHub repo in the next tweet!

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-04-12 17:02 UTC

  • OpenClaw-RL Repository and Free AI/ML Engineering PDF Guide

    By

    @akshay_pachaar

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    OpenClaw-RL Repo: github.com/Gen-Verse/OpenClaโ€ฆ If you want to learn AI/ML engineering, I have put together a free PDF (380+ pages) with 150+ core lessons. Download for free: dailydoseofds.github.io/ai-eโ€ฆ

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-04-12 13:35 UTC

  • OpenClaw-RL: Reinforcement Learning for Agent Model Weights
    OpenClaw-RL: Reinforcement Learning for Agent Model Weights

    By

    @akshay_pachaar

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    OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: – Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. – On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-04-12 13:35 UTC

  • High-Signal Trajectories and DPO for Agent Optimization

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    @akshay_pachaar

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    Good solution. Btw, Once youโ€™ve identified the high-signal trajectories, you can also pair them with counterfactual continuations (what the agent should have done at the point of failure) to construct preference pairs for DPO. So the signals don't just act as a debugging tool

    โ†’ View original post on X โ€” @akshay_pachaar

  • InsForge: Open-Source Backend Solution for AI Coding Agents
    InsForge: Open-Source Backend Solution for AI Coding Agents

    By

    @akshay_pachaar

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    AI agents suck at backend. They ship beautiful frontends in seconds, but completely fall apart the moment you ask for a database, auth, or storage. InsForge is an open-source solution, built natively for AI coding agents and editors. It exposes backend primitives like databases, auth, storage, and functions through a semantic layer that agents can understand, reason about, and operate end-to-end. It works with any agent you already use, whether that is Cursor, Claude Code, Codex, OpenClaw, or Hermes. 100% open source. GitHub repo: github.com/InsForge/InsForge (don't forget to star ๐ŸŒŸ)

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-04-11 12:40 UTC

  • Agent Harness: The Infrastructure Bet Defining AI Architecture
    Agent Harness: The Infrastructure Bet Defining AI Architecture

    By

    @akshay_pachaar

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    What does every big company think about the agent harness? Anthropic, OpenAI, CrewAI, LangChain. They all build agents. They all wrap their models in infrastructure to make them useful. They each call it the harness. But they agree on one thing. And disagree on everything else. The agreement: the model is not the product. The infrastructure around the model is. The disagreement: how much of that infrastructure should exist. This is the most important architectural bet in AI right now. And each company is placing a different one. ๐—”๐—ป๐˜๐—ต๐—ฟ๐—ผ๐—ฝ๐—ถ๐—ฐ bets on the model. Their harness is deliberately thin. A "dumb loop" that assembles the prompt, calls the model, executes tool calls, and repeats. The model makes all the decisions. The harness just manages turns. Their bet: as models get smarter, you need less infrastructure, not more. ๐—ข๐—ฝ๐—ฒ๐—ป๐—”๐—œ takes a similar but slightly thicker approach. Their Agents SDK is "code-first," meaning workflow logic lives in native Python, not in some graph DSL. But they add more structure: strict priority stacks for instructions, multiple orchestration modes, and explicit agent handoff patterns. ๐—–๐—ฟ๐—ฒ๐˜„๐—”๐—œ adds a deterministic backbone. Their Flows layer handles routing and validation with hard-coded logic, while their Crews handle the autonomous parts. Intelligence where it matters, control everywhere else. ๐—Ÿ๐—ฎ๐—ป๐—ด๐—š๐—ฟ๐—ฎ๐—ฝ๐—ต bets on explicit control. The harness encodes the logic. Every decision point is a node in a graph. Every transition is a defined edge. Planning steps, routing strategies, multi-step workflows are all spelled out in the harness, not left to the model. Notice the spectrum. On one end: trust the model, keep the harness thin. On the other: encode the logic, make the harness thick. And here's where it gets interesting. The scaffolding metaphor makes this concrete. Construction scaffolding is temporary infrastructure that lets workers reach floors they couldn't access otherwise. It doesn't do the building. But without it, workers can't reach the upper floors. The key word is temporary. As the building goes up, scaffolding comes down. Manus demonstrated this perfectly. They rebuilt their agent five times in six months. Each rewrite removed complexity. Complex tool definitions became simple shell commands. "Management agents" became basic handoffs. The scaffolding did its job. So they removed it. This is also why Anthropic regularly deletes planning steps from Claude Code's harness. Every time a new model version ships that can handle something internally, the corresponding harness logic gets stripped out. But there's a catch. Models are now trained with specific harnesses in the loop. Claude Code's model learned to use the exact scaffolding it was built with. Change the scaffolding, and performance drops. The worker trained on THIS scaffolding. Swap it out, and they stumble. So the field is converging on a principle: Build scaffolding that's designed to be removed. But remove it carefully, because the model learned to lean on it. The "future-proofing test" for any agent system: if dropping in a more powerful model improves performance without adding harness complexity, the design is sound. Two products using the exact same model can perform completely differently based on this one decision: how thick is the harness? LangChain changed only the infrastructure (same model, same weights) and jumped from outside the top 30 to rank 5 on TerminalBench 2.0. The model didn't improve. The scaffolding around it did. The article below is a deep dive on agent harness engineering, covering the orchestration loop, tools, memory, context management, and everything else that transforms a stateless LLM into a capable agent. Akshay ๐Ÿš€ (@akshay_pachaar) x.com/i/article/204073208484โ€ฆ โ€” https://nitter.net/akshay_pachaar/status/2041146899319971922#m

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-04-10 12:51 UTC

  • Unsloth Studio Colab Notebook for LLM Fine-tuning

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    @akshay_pachaar

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    Here's the notebook: colab.research.google.com/giโ€ฆ If this was helpful, reshare with your network. Find me โ†’ @akshay_pachaar โœ”๏ธ For more insights and tutorials on LLMs, AI Agents, and Machine Learning!

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-04-10 07:49 UTC

  • Free Google Gemma 4 Fine-tuning with Unsloth Colab Notebook

    By

    @akshay_pachaar

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    Fine-tune Google Gemma 4 completely FREE! All you need is a browser and 500+ models to choose from. The process is simple: 1. Open the Unsloth Colab notebook 2. Pick your model and dataset 3. Hit start training And you're done!

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-04-10 07:49 UTC

  • Advisor Models: Pairing Weak and Strong AI for Cost-Efficient Intelligence
    Advisor Models: Pairing Weak and Strong AI for Cost-Efficient Intelligence

    By

    @akshay_pachaar

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    this is one of the most important ideas in AI right now, and it just got two independent validations. yesterday, Anthropic shipped an "advisor tool" in the Claude API that lets Sonnet or Haiku consult Opus mid-task, only when the executor needs help. the benefit is straightforward: you get near Opus-level intelligence on the hard decisions while paying Sonnet or Haiku rates for everything else. frontier reasoning only kicks in when it's actually needed, not on every token. back in February, UC Berkeley published a paper called "Advisor Models" that trains a small 7B model with RL to generate per-instance advice for a frozen black-box model. same idea. two very different implementations. the paper's approach: take Qwen2.5 7B, train it with GRPO to generate natural language advice, and inject that advice into the prompt of a black-box model. the black-box model never changes. the advisor learns what to say to make it perform better. GPT-5 scores 31.2% on a tax-filing benchmark. add the trained advisor, it jumps to 53.6%. on SWE agent tasks, a trained advisor cuts Gemini 3 Pro's steps from 31.7 to 26.3 while keeping the same resolve rate. training is cheap too. you train with GPT-4o Mini, then swap in GPT-5 at inference. the advisor even transfers across families: a GPT-trained advisor improves Claude 4.5 Sonnet. Anthropic's advisor tool takes a different path to the same idea. Sonnet runs as executor, handles tools and iteration. when it hits something it can't resolve, it consults Opus, gets a plan or correction, and continues. Sonnet with Opus as advisor gained 2.7 points on SWE-bench Multilingual over Sonnet alone, while costing 11.9% less per task. Haiku with Opus scored 41.2% on BrowseComp, more than double its solo 19.7%. it's a one-line API change. advisor tokens bill at Opus rates, and the advisor typically generates only 400-700 tokens per call. blended cost stays well below running Opus end-to-end. both approaches point at the same thing: you don't need the most powerful model on every token. you need it at the right moments, for the right inputs. Paper: arxiv.org/abs/2510.02453 Code: github.com/az1326/advisor-moโ€ฆ Claude (@claudeai) We're bringing the advisor strategy to the Claude Platform. Pair Opus as an advisor with Sonnet or Haiku as an executor, and get near Opus-level intelligence in your agents at a fraction of the cost. โ€” https://nitter.net/claudeai/status/2042308622181339453#m

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-04-10 05:46 UTC

  • Understanding AI Agents: Components and Orchestration Explained

    By

    @akshay_pachaar

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    A simple way to think about AI agents: LLM = reasoning Tools = actions Memory = context Orchestration = the loop The first three are components. The last one is what makes them an agent.

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-04-09 20:21 UTC