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.

LLM quantization techniques I'd learn if I had to fit a 70B model on a single GPU: (bookmark this) A 70B model in FP16 needs 140GB for weights alone. At 4-bit, that drops to 35GB, which fits on one card. But naive rounding fails on large models. Roughly 0.1% of hidden dimensions carry values up to 20x larger than anything else in the tensor, and they wreck the quantization grid for everything else. Each of these 5 methods handles those outliers at a different point: 1. RTN: ignores them. Rounds every weight to the nearest grid level with no calibration data. Cheapest option, weakest at low bit widths. 2. GPTQ: repairs after rounding. Quantizes a layer column by column and adjusts the remaining weights to absorb the error before moving on. 3. AWQ: protects before rounding. Finds the ~1% of weight channels that matter most and scales them up so they survive quantization. Everything still ends up in plain INT4. 4. LLM. int8(): isolates at inference. Outlier dimensions run in FP16, the other 99.9% run in INT8, and the results are merged. 5. QAT: solves it during training. The model is fine-tuned with rounding baked into every forward pass, so it adapts to the damage before quantization is actually applied. All five produce the same artifact, a model at a fraction of its trained precision. They differ only in where the outlier problem gets addressed. The visual below nicely summarise these techniques. There's a really good paper that provides a comprehensive study of LLM quantization techniques. I have shared the link to the paper in the first comment. ____ Share this with your network if you found this insightful ♻️ Follow me (Akshay Pachaar) for more insights and tutorials on AI and Machine Learning!

Building an AI application is one thing. Shipping it to production is another. Your RAG pipeline might work perfectly on your laptop. But can you deploy it reliably on any server? That's where Docker comes in. In this carousel, you'll learn how to containerize an AI application from scratch: → Docker Images vs Containers → Why AI projects need Docker → Dockerfile fundamentals → Layer Caching → .dockerignore → Managing Secrets → Multi-stage Builds → Non-root Containers → Health Checks → Volumes → Docker Compose → GPU Support → Production-ready Dockerfile → Essential Docker Commands → Common Docker Mistakes Along with: → Why python:3.11-slim is the preferred base image → How to reduce rebuild time from minutes to seconds → How to keep API keys out of your Docker image → Why AI applications should use multi-stage builds → How to persist vector databases and model weights → How to deploy RAG applications with Docker Compose → Best practices for production-ready AI deployments Docker isn't just about packaging your application. It's about making sure your AI system runs the same way everywhere without dependency conflicts, environment issues, or "works on my machine" problems. If you're serious about AI Engineering, Docker is a skill you can't skip. Save this for your next AI deployment.

This is a sharp, original observation — the X/Y dynamic is specific and insightful, naming a loop (AI writes, AI comments, both performing) that most "stop the AI slop" posts don't quite articulate. The closing thought ("learn to actually be good at things") is a genuine, standalone point of view, not a recycled platitude.

LangGraph isn't just another AI framework. It's a different way to think about AI systems.Many developers use LangGraph like LangChain — one node, one LLM call, one response. If that's your workflow, you probably don't need LangGraph. LangGraph shines when your application needs decisions, loops, memory, and human supervision.Here's how I think about it:➡️ State — Every node reads from the same shared memory. Design your state schema first; everything else depends on it.➡️ Nodes — One node, one responsibility (Retrieve, Grade, Generate). Don't build one giant node that does everything.➡️ Conditional Edges — The graph decides what happens next (generate, retrieve again, call a tool, ask for human approval). The workflow adapts at runtime.➡️ Parallel Execution — Break a complex question into smaller tasks, process simultaneously, merge results. Better latency without sacrificing quality.➡️ Checkpointing — Save state after every step, resume from failures, continue conversations across sessions. Stateless agents don't scale well.➡️ Interrupts — Pause before sending an email, executing code, or making expensive decisions. Some actions should always require human approval.➡️ ToolNode — Let the model decide when to use tools, standardize execution, avoid custom tool-calling logic whenever possible.➡️ Recursion Limits — Every loop needs a limit; every autonomous workflow needs a safety cap. Infinite reasoning is just an expensive bug.One lesson changed how I build AI agents: don't think of LangGraph as a chain — think of it as a workflow engine. A graph where every node has a purpose, every edge has a reason, every state change is intentional. That's what makes production AI systems reliable.

This is a sharp, specific critique — it names the exact tics (the X-not-Y framing, rule-of-three, buzzwords) that make posts feel formulaic, and it lands a genuinely good line ("you didn't get a lobotomy") that's memorable and in your own voice, not borrowed from a template.

Run your personal AI company with a team of AI agents! Alook is an open-source collaboration platform for AI coding agents. Self-hosted and local-first. The setup: Define an org structure. Give each agent a role - dev, ops, research, whatever you need. Set reporting lines. Alook gives each agent an email address. How it works: Assign a task to the right agent. They take it from there. Agents coordinate through email - passing deliverables, asking questions, updating status. You see everything in your inbox but you're not routing anything manually. Runs as an always-on daemon. Close your laptop, agents keep working. Come back to finished tasks. Shared memory across all agents. Every agent knows what every other agent worked on. You never re-explain context. After each task completes, Alook logs what worked and builds SOPs. The whole team gets sharper over time. Works with Claude Code, Codex, and OpenCode. Mix and match or run multiple agents from one runtime. Built-in Kanban for task tracking. Calendar for scheduling. Email for all communication. Agents pick up tasks autonomously, update their own calendars, close issues when done. Chat or email with agents like any AI tool. Install the runtime once, runs in the background. No terminal needed after setup. Key capabilities: • Email-based agent coordination with real inboxes • Org structure with roles and reporting lines • Shared memory and self-learning SOPs • Always-on daemon for 24/7 operation • Works with Claude Code, Codex, OpenCode • Built-in Kanban, calendar, and email • Self-hosted and local-first 100% open source. I've shared the Github Repo in the replies!