Mircea Trifan @mobilepixel

Great Python Programming! Finite Element Method with Python: An Essential Course with AI Enhanced Approaches! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Books #Coding #100DaysofCode geni.us/Great-Python-P…

STANFORD JUST RELEASED A FREE COURSE ON ONE OF THE MOST CRITICAL SKILLS IN AI RIGHT NOW: BUILDING SELF-IMPROVING AGENTS. The entire curriculum is focused on self-correction. It skips the basic API wrappers and gets straight into the mechanics of frontier reasoning. If you want to understand how autonomous systems actually learn from their actions, the syllabus covers the exact stack being used today: → Test-time compute and verifiers → Constitutional AI and RL → Planning, memory, and tool execution → Architectures for deep research agents It is highly project-based. You watch the lectures, build alongside the researchers teaching them, and walk away with a functional self-improving architecture. I've put the full playlist in the 🧵↓

I've had my copies for a few weeks now, and it looks like orders are being shipped with more regularity now. 🎉 I hope you find the book useful!

Anthropic hired this engineer at $250K-$750K a year because he knows how to build harnesses for multi-agent systems In this 15-minute workshop, he shows exactly how to build one from scratch AI → Agents → Harness → Loops → Graphs step 1 → start with the Claude Agent SDK - the harness handles loops, context, and sandboxing step 2 → separate the brain from the hands - reasoning in one place, tools in a sandbox, 60% faster to first token step 3 → run it server-side and log every step - close your laptop and it keeps running, crashes resume from the log step 4 → make failure cheap - retry dead sandboxes and replay lost context instead of starting over step 5 → turn yesterday's logs into new memory and skills - the harness wakes up smarter Anthropic calls this "dreaming" Most people spend weeks building this by hand You don't have to Bookmark and watch it Then read the full harness engineering guide below ↓

rari @0xwhrrari

3 weeks ago

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I’m excited to finally announce the newest edition my Stanford course 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿. It has been 9 months in the making. Last November, with the release of Claude Opus 4.5, coding agents experienced a step function improvement in capability. We all felt it. The LLMs were more powerful, could reason for longer, solve harder tasks. This year’s iteration of my course reflects the 2026 metamorphosis of software engineering. My core belief is simple: AI-native developers of the LLM era are going to become the most important members of any software organization. I have designed my course to train this next generation of engineers. 𝗪𝗵𝗮𝘁’𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝗵𝗶𝘀 𝘁𝗶𝗺𝗲 𝗮𝗿𝗼𝘂𝗻𝗱 First, 85% of my Fall 2025 class material is being thrown out. The Fall 2026 syllabus reflects the core capabilities AI-native engineers must have: agent skills, advanced context engineering, MCP portals, agent-ready codebase principles, agentic code review, security, parallelizing background agents, software factories, and more. Second, I am going to teach my students how to have software taste. Every student will be required to ship pull requests to production-grade, real-world codebases. The course is collaborating with the top open-source AI repos who will offer support and mentorship to students on how to meaningfully contribute to their projects. This has never been done before in any university course so I am incredibly grateful to our OSS Partners: @browserbase, @HeyGen, @CopilotKit, @semgrep, @OpenHandsDev, @milvusio, @marimo_io, Pi, @crewAIInc, @warpdotdev, @vercel, @cmux, @arizeai, @UnslothAI, and @anyscalecompute. 𝗪𝗵𝗮𝘁’𝘀 𝘀𝘁𝗮𝘆𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 I’m fortunate to again have AI software engineering leaders and founders as guest speakers to share their learnings from building top coding agent products. Thank you to @leerob from @cursor_ai, @bcherny of @claudeai code, @EnoReyes of @FactoryAI, @silasalberti of @cognition, @0xine of @semgrep, Rajesh Bhatia of @Cloudflare , @amasad of @Replit, and @eladgil. All resources will be available online. All classes will be available to the public. 9/22 on Stanford campus. See you in class.  themodernsoftware.dev

Andrew Ng just released a free 2-hour course on building agentic skills from scratch with Anthropic: 0% → 00:15 - build your first agent skills with Claude 30% → 22:32 - work with Claude's pre-built skills 55% → 41:07 - learn skills vs tools, MCP, and sub-agents 100% → 01:06:06 - create skills for long-running agents This 2-hour course can replace 10 paid courses on agent building Taught together with Anthropic engineers Bookmark it and watch tonight Then read the full guide below ↓

Lunar @LunarResearcher

a month ago

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How have software engineering fundamentals changed with agentic coding? Here is our AI Engineering Skills map for software engineering fundamentals. x.com/i/article/2093…

BMAD, Spec Kit, OpenSpec, Kiro: A Field Guide to Getting Real Work Out of AI Coding Agents by @roanbrasil medium.com/@roanmonteiro/…

OpenAI Computer History by Rick Hightower medium.com/@richardhighto…

Stanford Just Put an $850K/Year Skill on YouTube by @DareToBe_Better medium.com/dare-to-be-bet…

Anthropic Just Launched Claude Academy (And I Earned My 5 Badges) by @joenjenga_ medium.com/ai-software-en…

WebMCP (Web Model Context Protocol): Agents are learning to browse better by @abvijaykumar abvijaykumar.medium.com/webmcp-web-mod…

Andrew Ng just released 2-hour course on 100% Graph engineering: 1 prompt → 100 agents → loops → graphs from scratch: 10% → 9:14 - build your first agent from scratch 30% → 33:11 - Loop engineering 55% → 1:02:46 - Graph engineering 75% → 1:30:15 - agents that rewrite themselves 100% → 1:49:05 - full graph system that work without you most graph tutorials stop at the diagram - this one has you running one in the first 20 minutes watch this brilliant course, build the graph - then read the full architecture below ↓

codila @0xCodila

2 months ago

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The Mathematics of Large Language Models — A Readable Guide to LLMs, Transformers, Diffusion, Neural Networks, and Generative AI: amzn.to/4wJgS16

Don't waste 2 years learning to code. Thariq Shihipar, the Anthropic engineer who built Claude Code tells you what to learn instead. 1 hour course. Free: 00:00 - build AI agent with loops 40:47 - prompting Claude with HTML 1:15:07 - Anthropic's coding playbook Writing code is the old job. Building AI agent loops is the new one. Bookmark now & read the article below to build your own AI agent with loops & graphs.

Roan @RohOnChain

2 months ago

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Creator of Claude Code just dropped a free masterclass on agent engineering with Opus 5 from 0% to 100%: “80% of our engineers are using self-improving loops” 10% → 3:38 - why they deleted 80% of the system prompt 30% →6:55 - delete your CLAUDE.md every 6 months 55% →17:44 - one prompt, 11 days, entire codebase rewritten 80% → 21:59 - the prompt that's been running for 15 days 100% →25:27 - two promts that spawn 1,000 agents most people are still writing longer prompts - he's deleting them and running 1,000 agents instead bookmark & watch - then read the full agent orchestration playbook below ↓

codila @0xCodila

2 months ago

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We're hiring a Research Engineer at @Arsenal ⚽🔴⚪ to work directly with our Men's First Team! We're building state-of-the-art AI models for the football domain. This role will focus on building the application layer for our research to advance coaching and analysis workflows.

IBM just released a 1-hour course on building agentic knowledge graphs from scratch: • 00:00 - Introduction to knowledge graphs • 05:35 - Building your first agentic graph • 19:59 - Agentic memory powered by graphs • 30:39 - Graphs for multi-agent orchestration This 1-hour watch will replace 10 paid courses on agentic engineering. Watch it today, then learn how to become a knowledge graph engineer in the article below.

Codez @0xCodez

2 months ago

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Boris Cherny just dropped 7-page PDF on Graph Engineering - how 4 Claude prompts replace 4 trained ML models The twist: your agent's memory dies with the context window. A knowledge graph makes it permanent - and now you build one with prompts, not ML engineers. here's 4 prompts, step by step: prompt 1 → extraction - Haiku pulls entities + relations - one call per doc - no NER, no labeled data prompt 2 → resolution - Sonnet merges duplicates string matching will never catch - two different names, same person prompt 3 → summarization - Sonnet builds profiles from multiple sources - facts that never appeared in the same document prompt 4 → querying - feed the graph to Sonnet - every answer cites a specific edge - no hallucination how to wire this into agents today: step 1 → shared memory for multi-agent teams - workers read/write one graph - orchestrator's context stays clean step 2 → grounding layer for eval loops - evaluator checks facts against graph edges, not vibes the result: one Pydantic schema replaces weeks of ML training per domain - precision 1.00 - 10k docs cost under $10 this 7-page PDF is what comes after loop engineering bookmark this, then read the article below ↓

vartekx @vartekxx

2 months ago

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Two Anthropic seniors just made Karpathy's loop 1000x better with "Graph Engineering" - dropped 11-page PDF the shift: the agentic systems got 1000x better the moment you wired agents into a graph here's the playbook in 6 steps: step 1 → build one loop: generate, critique, revise - one self-review cycle beats a smarter model with none step 2 → add tools: search, code execution, database - thinking without tools is hallucinating step 3 → go parallel: spin up agents in separate worktrees - same repo, different branches, no conflicts step 4 → add a graph: agents write findings as typed nodes and edges - not transcripts - every claim keeps its source step 5 → ground your evaluator: it checks claims against graph edges, not vibes - "Triple not found" beats "seems off" step 6 → the graph survives every session - your agents stop rebuilding context from scratch the result: Karpathy ran 1 agent in 1 direction - this system runs 1,000 with shared memory - same model, it's the architecture read this 11-page PDF and paste it into your Claude - you won't regret it bookmark - then read the article on building graphs from scratch ↓

codila @0xCodila

2 months ago

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46 333 2K 5.3M 6K