I gave my AI coding agent a formal performance review. It got an A-. I got a B.Claude Code saves a transcript of every session on your machine. 31 days of mine, sitting in a folder. So I built skiplevel, an open source tool that reads them and reviews both parties.What the review found:The agent said "you're absolutely right" 56 times. It read the same file 30 times in one session. It also generated an itemized receipt showing $164 of usage went to re-reading files it had already read.My side was not clean either. 339 interruptions. 3,025 words typed in all caps. I apologized to a language model 7 times, which it noted in my file.Everything runs locally in about 2 seconds, nothing uploads, and it works on Claude Code, Codex CLI, and opencode. The grading rubric is deterministic and documented, no LLM involved.Built the whole thing with Claude Code over a weekend. So Claude wrote the tool that reviews Claude, then graded itself A-.Free and open source, link in comments. Run it and tell me your verdict

Dense, accurate technical content — six correctly explained model formats, a specific and verified vLLM behavior, and a real open-source tool (SIE) with accurate feature description. This is well-researched, substantive material.

I built a $10,000 app with one Claude Fable 5 prompt.No code. No dev team. No babysitting the AI.Here's the exact workflow, step by step ↓This works for any app you've been putting off.A client portal. A booking tool. An internal dashboard.The flow is the same every time.But first, here's why one-prompt builds never worked before:→ You hand AI a task, then go check what it broke→ It stops at 80% asking for a pat on the head→ It guesses through every detail you didn't specify→ It says "done" without ever testing a thingFable 5 killed all four.I tested it for 24 hours on my own money to make sure.Here's how to run it yourself:𝐒𝐭𝐞𝐩 𝟏 → Open Claude Code, load Fable 5, set effort to extra high.This is the mode built for long runs. Don't skip it.𝐒𝐭𝐞𝐩 𝟐 → Don't describe your app. Make Claude interview YOU.Tell it: "You're my technical co-founder. Interview me about the features, the data, and the design, one batch at a time. No code until I say go."𝐒𝐭𝐞𝐩 𝟑 → Answer its menus. Real checkboxes, right in the terminal.Let it push back. Mine talked me OUT of features so the build wouldn't balloon. That's it managing scope for you.𝐒𝐭𝐞𝐩 𝟒 → When it hands back the full spec, type /goal and paste that spec in.You never write the build prompt. The interview already wrote it for you.𝐒𝐭𝐞𝐩 𝟓 → Walk away. Seriously.It runs until the spec is real, then tests its own work end to end before it says done.I ran this exact flow on a scheduling hub for my service business.23 minutes. 119k tokens. One turn. A finished tool agencies quote five figures for.It even flagged its own weak spot before I could.That used to be my job.The truth is the build is no longer the hard part.If you can describe what you want, you can ship software now.That's the whole skill.This is the exact method I used to build the internal tool my team runs on now.If you want to build your own, I documented the whole build, plus every other Fable 5 use case I tested.

 Highly specific, verifiable, and useful — names the exact app, the exact pipeline (pretrain → SFT → DPO), hardware requirements, time estimates, and the "no coding/math needed" claim, which all check out against the actual product.

Woah...My Hermes Agent can now create LIVE web datasets on the fly 🤯 I ask for something. It fires up agents to research on the live web, verifies against real sources, and hands back a structured dataset that refreshes on a schedule. It's using TinyFish BigSet under the hood. And it's 100% Open Source. Most AI research still breaks at the same point: the data layer. Your agent can search, browse, and write a decent report. But when the data lives across GitHub repos, pricing pages, docs, job boards, and launch posts, you're still the one stitching it together manually. BigSet does the boring but important work: Infers the schema from your sentence Sub-agents fan out in parallel each fetching & verifying real data Deduplicates and builds a structured table Exports CSV or XLSX, refreshes anywhere from every 30 minutes to weekly So Hermes builds the dataset it needs before doing the analysis. I ask my Hermes agent: "Find AI agent products and open-source projects launched or updated in the last 7 days, with category, URL, pricing, target user, and why it matters." BigSet turns that into a real live dataset. And Hermes turns that into signal. The research bottleneck was never the reasoning. It was getting hallucination-free data into the agent's hands. Now that layer is open-source.

𝐋𝐋𝐌-𝐂𝐡𝐞𝐜𝐤𝐞𝐫 - 𝐋𝐋𝐌 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐓𝐨𝐨𝐥 𝐟𝐨𝐫 𝐘𝐨𝐮𝐫 𝐋𝐨𝐜𝐚𝐥 𝐒𝐲𝐬𝐭𝐞𝐦 Are you struggling to figure out which LLMs you can run locally on your system? LLM Checker CLI tool comes to the rescue. 𝐋𝐋𝐌 𝐂𝐡𝐞𝐜𝐤𝐞𝐫 𝐢𝐬 𝐚𝐧 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐂𝐋𝐈 𝐭𝐨𝐨𝐥 𝐭𝐡𝐚𝐭 scans your local system hardware (CPU, RAM, GPU) and recommends which LLMs you can run locally with Ollama If you build or deploy Local GenAI systems, LLM Checker is a must in your toolkit. 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 35+ Curated Models: Hand-picked catalog covering all major families and sizes (1B-32B) 4D Scoring Engine: Quality, Speed, Fit, Context — weighted by use case Multi-GPU Hardware Detection: Apple Silicon, NVIDIA CUDA, AMD ROCm, Intel Arc, CPU Calibrated Memory Estimation: Bytes-per-parameter formula validated against real Ollama sizes Zero Native Dependencies: Pure JavaScript — works on any Node.js 16+ system

The specific interview questions ("Why do embeddings work?", "What happens before the first token?"), the named concept list (transformers, tokenization, vector search, etc.), and the sharp observation about frameworks-vs-fundamentals are concrete and useful — this is genuinely good technical content.

The next step after Karpathy's autoresearch idea:Tuning an agent is mostly manual work, done by editing prompts, tools, and control flow by hand and rerunning the evals to see what moved.Researchers have been building several automated optimizers to do that outer loop instead.The underlying process is the same:- An LLM proposes a change- An evaluator scores it- And the proposer reads that result before proposing the next one.They differ in what they edit and what feedback they get to read.1) Berkeley built GEPA that optimizes the text of a system, like prompts, tool descriptions, or the agent's own code.Instead of collapsing a run into one scalar reward the way RL does, it reads the full execution trace (errors, reasoning, tool output), diagnoses why the run failed, and proposes a targeted fix.It also keeps every candidate that's best at some part of the task, not just the one with the highest average score. So a strong specialist survives even when a more balanced candidate beats it overall.That trace-level feedback lets it converge in hundreds of rollouts instead of the thousands that GRPO needs.2) AutoResearch, inspired by Karpathy, runs a narrower version of the loop, where a coding agent iterates on a program(.)md file, scores the outputs against fixed evals, and keeps whatever improves.3) Meta-Harness points the loop at the harness itself, the scaffolding code that decides what to retrieve, how to format it, and what state to keep between calls.The natural question is which of the three to use. And the answer is none of them alone.On the Frontier-CS benchmark, holding the model, thinking effort, and budget fixed, no optimizer wins everywhere. Across 10 tasks, GEPA led on 3, AutoResearch on 3, and Meta-Harness on 4.Each optimizer hill-climbs fast and then stalls, making most of its progress in a few iterations before flattening out.But the researchers found that handing the stalled candidate to a different optimizer breaks the plateau, since each one attacks the problem differently.To use this in practice, omni (open-source) already automates all of that.It runs every optimizer on a fraction of the budget, takes the best candidate, and hands it to a fresh optimizer to keep going.It scores 7.8 percentage points above the best standalone optimizer at the same budget, and finishes faster.The whole meta-optimizer is around ten lines in the optimize_anything API, the same interface these optimizers already run through.You can also point an agent at the gepa-ai/gepa repo and have it use the gepa-optimize-anything skill.I have shared the repo in the replies.____Share this with your network if you found this insightful ♻️Find me → Avi Chawla. Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

Describe a web task to Lightpanda's agent and an LLM drives a real headless browser. Run /save and it exports the session as PandaScript: deterministic vanilla JavaScript you replay with no model at runtime, credentials kept out of the script and the model's prompt. You prototype with the model, then ship the script without it. A from-scratch browser in Zig. 32.2k stars, AGPL-3.0.

A French engineer in Paris has spent 25 years writing the software the entire internet runs on. Almost nobody knows his name. He wrote the code that streams every YouTube video, every Netflix show, every TikTok clip. He wrote the code the cloud spins your virtual servers up on. He once beat a multi-million-dollar supercomputer at calculating pi - on a desktop PC. His name is Fabrice Bellard. Born in France. He never moved to Silicon Valley or raised a round. He just writes code. In 2000, at 28, he started FFmpeg - one library that decodes every audio and video format on every OS. Today it powers video almost everywhere - VLC, Chrome, phones, smart TVs. If you've watched anything on a screen this decade, FFmpeg touched it. In 2003 he started QEMU, an emulator that lets one operating system run inside another. He wrote it solo through 2005. KVM sits on top of it, and AWS, Google Cloud, and Azure all run virtual machines on infrastructure built around it. Then he just kept going: → Built TCC, a C compiler that boots a Linux kernel in under 15 seconds → Created JSLinux, a PC that runs Linux in your browser, in pure JavaScript → Released QuickJS, a JS engine that fits where V8 can't → Computed pi to 2.7 trillion digits on a $3,000 desktop, beating machines worth millions On top of all this, he co-founded Amarisoft in 2012 and remains its CTO, building the 4G and 5G base-station software used by carriers and labs worldwide. For 25 years Bellard has done the same thing - pick a brutally hard problem, crack it on his own, give the code away. He's barely online. His website is a flat list of projects with no styling, just titles and links. In December 2025, at 53, he shipped Micro QuickJS - a JavaScript engine that runs in about 10KB of RAM, small enough for a microcontroller. Still curious. Still building. Still shipping. Absolute legend.

Specific, concrete details anchor this — the named employer (Bizgenix AI Solutions), the named community (Cosmos) with stated activities (workshops, hackathons), and named technical domains (RAG systems, LLM applications, AI agents) are all verifiable-in-principle specifics, not vague claims

The fastest coding agent in the world is still slow if ten tasks are standing behind it in a queue.Parallel Code removes the queue.I think the more interesting shift in AI coding is from chatting with one model to scheduling work against a repository.A bug fix, a test suite, a dependency upgrade, a refactor and an experimental implementation often have very few dependencies between them.There is no fundamental reason to serialize all five through one terminal.Parallel Code maps the problem onto primitives Git already understands:> branch = task> worktree = isolated filesystem> agent session = worker> diff = result> merge = selectionCreate a task and it creates a new branch, spins up a separate Git worktree, links ignored directories such as node_modules, and launches the coding agent inside that workspace.Several tasks can now progress without editing the same working files.When they finish, you inspect the diffs, leave comments, merge the useful branches and discard the rest. There is also an Arena mode for running competing implementations and comparing the results.Importantly, Parallel Code did not invent parallel coding agents.Claude Code already supports isolated worktree sessions. Codex also has built-in parallel agents, diff review and worktree isolation.That actually makes this repo more interesting, not less.It takes a pattern that is quickly becoming native across coding tools and exposes it as a free, open-source control surface for several existing CLIs.The point is not that you need Claude, Codex and Gemini simultaneously.The point is that task isolation should be independent of whichever agent you prefer.This feels less like pair programming and more like an operating system:> Fan out the work.> Isolate the processes.> Inspect the outputs.> Commit the good state.Models made individual coding tasks faster.Now the workflow around them is learning to become parallel.

7 open source repos that do what cursor, perplexity, and v0 do combined zero waitlists. zero api markups. zero vendor lock in. save the list: » openhands // agentic coding platform. plans, edits files, runs terminal commands, browses the web on its own. mit https://lnkd.in/gmxhbTCH » zed // rust-native ai editor. gpu-accelerated, ships the open agent client protocol so any agent can plug in. 86k stars https://lnkd.in/g_w75viP » codex // openai's official terminal coding agent. runs in network-isolated containers for secure work. 94k stars, apache 2.0 github.com/openai/codex » scira (formerly miniperplx) // minimalist perplexity alternative. citations, focus modes, built on vercel ai sdk. 11.7k stars https://lnkd.in/g7KdmvQg » morphic // open source answer engine with generative ui. streams charts and comparisons inline with the answer. mit github.com/miurla/morphic » december // self-hosted bolt.new. $5 in api credits gets you 50+ full app generations. docker deploy in one command https://lnkd.in/gihiCf7B » libra // cloudflare-native v0 and lovable alternative. serverless, free tier friendly, byok https://lnkd.in/gVg9cPyU closed source is losing this race quietly. star them before your feed catches on.

Speed up LLMs with LMCacheWhat is LMCache?LMCache is an LLM serving engine extension to reduce TTFT and increase throughput, especially under long-context scenarios.How does LMCache work?By storing the KV caches of reusable texts across various locations, including (GPU, CPU DRAM, Local Disk), LMCache reuses the KV caches of any reused text Thus, LMCache saves precious GPU cycles and reduces user response delay.How effective is LMCache?By combining LMCache with vLLM, developers achieve 3-10x delay savings and GPU cycle reduction in many LLM use cases, including multi-round QA and RAG.Activate to view larger image,

Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose. Rohit Prsad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think!

If your LLM is slow... 10 ways to fix them!👇 Many apply wrong fixes! Mainly because it solves a different problem than the one you actually have. Most production latency problems aren't model problems. They're system problems wearing a model's costume. Here's the playbook: 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 (𝗠𝗟 + 𝗟𝗟𝗠) 1️⃣ Quantization → 8x less data to move per token 2️⃣ Pruning + distillation → smaller, faster, often better 3️⃣ Compilation (FlashAttention, TensorRT) → 2 to 4x free 4️⃣ Caching at three layers (request, semantic, prompt) 5️⃣ Use a smaller model (the lever no one wants to pull) 𝗟𝗟𝗠-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 6️⃣ KV caching → the foundation of all serving 7️⃣ PagedAttention → 2 to 4x more concurrent users 8️⃣ Speculative decoding → 2 to 3x faster decode 9️⃣ Continuous batching → throughput protection under load 🔟 The serving path itself (gRPC, streaming, region) But the techniques aren't the unlock! 𝗧𝗵𝗲 𝘂𝗻𝗹𝗼𝗰𝗸 𝗶𝘀 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗶𝗰𝗵 𝗼𝗻𝗲 𝘁𝗼 𝗿𝗲𝗮𝗰𝗵 𝗳𝗼𝗿! 1. If TTFT is the issue, prefill (compute-bound) is your bottleneck. Reach for prompt caching, FlashAttention, or a smaller model. 2. If TPOT is slow, decode is the bottleneck and it's memory-bound. Quantization, speculative decoding, and KV cache compression do the heavy lifting. 3. If throughput collapses under load, you're queueing. Continuous batching, PagedAttention, and more replicas are how you stop it. Please match the technique to the symptom. Full playbook: https://lnkd.in/gZUSvgRs Definitely worth reading to understand it well Give you 5 minutes to it and know so much more! ♻️ Repost if you found it helpful! ➕ We often discuss real-world AI/ML here 👇 ➕ Join 52,000+ AI/ML builders here: https://lnkd.in/ds_SzEUH

The X/Y dynamic is a sharp, original observation — it names a specific loop (AI writes, AI comments, both performing for the same insecurity) that cuts deeper than a generic "stop the AI slop" complaint. The closing line is a genuine standalone point of view.

This is literally one of the best LLM playlists out there!43 lectures from Vizuara where you build an entire LLM from scratch, then finetune it.You're writing the tokenizer yourself, coding attention from scratch, building the transformer block piece by piece.Here's everything it covers:Foundations→ LLM basics, pretraining vs finetuning, what transformers are→ How GPT-3 really works + the 3 stages of building an LLMData Pipeline→ Coding a tokenizer from scratch→ Byte Pair Encoding (the actual GPT tokenizer)→ Input-target pairs, token embeddings, positional embeddings→ The complete data preprocessing pipelineAttention Mechanism (the deep part)→ Simplified attention → self-attention with key/query/value→ Causal self-attention, coded from scratch→ Multi-head attention, both the intuition and the full mathBuilding the Architecture→ Birds-eye view of the LLM architecture→ Layer normalization, GELU activation, shortcut connections→ Coding the entire transformer block→ Building the actual 124M parameter GPT-2 modelTraining→ Defining the loss function + evaluating performance→ Coding the full pretraining loop→ Decoding strategies: temperature scaling, top-k sampling→ Saving/loading weights, loading pretrained GPT-2 weightsFinetuning→ Classification finetuning (spam classifier, built from scratch)→ Instruction finetuning with Alpaca-format prompts→ Data batching, dataloaders, the full finetuning training loop→ Evaluating the finetuned model with Ollamamost complete free resource on LLMs I've found.Heads up: quite a few of the lectures were recently moved to Members only, so you may need a YouTube membership to unlock the full series. Worth every penny.

Agent harnesses that learn from experience! MemoHarness is a new framework that makes agent harnesses adaptive rather than static. First, what's a harness. It's the external control layer that wraps a base LLM and turns it into a functional agent - managing context, tools, orchestration, memory, decoding, and output handling. Things like your CLAUDE. md, tool configurations, system prompts, memory strategies. Most agents use one fixed harness for every task regardless of what the task actually is. That's the problem MemoHarness addresses. A single static harness optimized for code generation isn't necessarily the right configuration for shell commands or analytical reasoning. But no one rebuilds the harness per task - it's too expensive and there's no principled way to do it. MemoHarness decomposes the harness into six editable control dimensions and treats execution history as a learning signal. After each run, it stores two types of knowledge in a dual-layer experience bank: per-case diagnoses of what went wrong in specific runs, and distilled global patterns extracted across many runs. For each new task, it retrieves relevant past experience and adapts the harness accordingly - no test-time labels, no additional feedback loop, no search required. The cost concern is real but addressed: when retrieved experience is cacheable, the additional context stays cost-competitive with fixed harnesses. Evaluated across shell-agent, code-generation, and analytical-reasoning benchmarks, MemoHarness improves over fixed harnesses and shows transfer to unseen task suites and base models. The broader point: most automatic improvement methods optimize prompts, pipelines, or workflows. The harness itself - the layer that controls how the agent processes everything - is usually left static. This paper makes the case that execution experience is a practical substrate for building harnesses that adapt. I've shared the link to the paper in the replies!

Your LLM can reason better without any fine-tuning!optillm is an OpenAI API-compatible proxy that implements 20+ optimization techniques to improve LLM accuracy on reasoning tasks without training or fine-tuning.The concept: Instead of one API call, optillm makes multiple calls using different techniques and combines the results. You're trading compute for accuracy - more API calls, higher cost, slower response, but better results.How it works: optillm sits between your OpenAI client and the LLM API. You control which technique by prepending a slug to the model name. With Mixture of Agents, optillm makes 3 parallel API calls with different approaches, synthesizes them, and returns the best answer.The tradeoff: A query that takes 1 API call and 2 seconds now takes 4 calls and 5 seconds. Token cost goes up 4x. But accuracy jumps significantly on reasoning tasks.Results show the gains. Mixture of Agents using gpt-4o-mini matches GPT-4 on Arena-Hard-Auto. PlanSearch achieves 20% higher pass@5 on LiveCodeBench.Available techniques:• Mixture of Agents: Multiple models critique each other• Monte Carlo Tree Search: Explores decision trees• PlanSearch: Searches candidate plans before executing• Best of N: Generates multiple responses, picks best• Chain-of-Thought with Reflection: Structured thinking and output• Self-Consistency: Multiple reasoning pathsWorks with 100+ models via LiteLLM. You can combine techniques in pipelines or run them parallel.The insight: Spend more computation at query time to get better results without training. Works for benchmarks, offline tasks, critical queries. Not for real-time production.