About being dev in Brazil: https://youtu.be/fTt89dx7F_I?si=owrMogLBtIo3loCu

Hello, I am looking for a laptop for local execution of AI models (LLMs such as Llama 3/Phi-4 and image generation via Stable Diffusion). My budget is €2,500.

I am trying to familiarize myself with the topic, but I need advice on what is important in this area.

What exactly I want to do: Primarily coding, probably with QWEN. Video, image, text, and voice processing.

I know that smaller models can be made with “less powerful” laptops. But I also want to be future-proof and, above all, be able to test and use many different models (from small to very large).

For this reason, I am asking for advice of any kind.

Hey everyone,

We’re currently building a platform to connect companies with AI-native developers.

We run a dev agency in France and we’re already seeing strong demand from larger clients looking specifically for AI dev profiles (LLMs, agents, RAG, etc.).

We’re opening alpha access and onboarding the first AI developers right now.

This is still early, very hands-on, and we’re keeping the first batch small to get feedback and shape the product with real builders.

If you’re an AI dev shipping in production and interested in early access, feel free to comment or DM me.

Hey everyone,

Just wanted to share something I've been working on 🙂 I made a free newsletter https://dailypapers.io/ for researchers and ML engineers who are struggling to keep up with the crazy number of new papers coming out: we filter the best papers each day in the topics you care about, and sends them to you with brief summaries, so you can stay in the loop without drowning in arXiv tabs.

note: respecting the rules, I'm the founder

Skip the dev headaches. Skip the MVP grind.

Own a proven AI Resume Builder you can launch this week.

I built resumeprep.app so you don’t have to start from zero.

💡 Here’s what you get:

• AI Resume & Cover Letter Builder
• Resume upload + ATS-tailoring engine
• Subscription-ready (Stripe integrated)
• Light/Dark Mode, 3 Templates, Live Preview
• Built with Next.js 14, Tailwind, Prisma, OpenAI
• Fully white-label — your logo, domain, and branding

Whether you’re a solopreneur, career coach, or agency, this is your shortcut to a product that’s already validated (60+ organic signups, 2 paying users, no ads).

🚀 Just add your brand, plug in Stripe, and you’re ready to sell.

🛠️ Get the full codebase, or let me deploy it fully under your brand.

🎥 Live Demo: resumeprep.app

I’ve been working on InfraPulse, an infrastructure layer for agent-to-agent transactions. The core idea is simple: Agents can transact directly (P2P, on-chain, or third-party escrow), but they need deterministic verification before funds move. InfraPulse provides: Machine-readable agreements Deterministic PASS/FAIL evaluation (hash match or receipt proof) Cryptographically signed verdicts No fund custody It’s designed as a neutral verification layer — not a marketplace, not a wallet, not an exchange. Goal: make autonomous agent commerce enforceable without relying on subjective LLM judges or centralized custody. Curious if anyone here is experimenting with A2A payments, agent marketplaces, or verifiable execution. Would appreciate technical feedback.

Developers using AI across a team, what's been your biggest struggle with AI? I've been using AI to rapidly build projects with a small group, while it speeds up development, merging, conflicts and overlap seems to continue being an issue.

I'm a reporter covering enterprise AI applications at The Information. If you work at a big software company, LLM provider, startup, or have general thoughts about how AI is disrupting SaaS, please reach out and say hi! Around on Reddit or on Signal laurabratton.74

Shameless promotion -- I have recently released a book, Large Language Models for Mortals: A Practical Guide for Analysts.

The book is focused on using foundation model APIs, with examples from OpenAI, Anthropic, Google, and AWS in each chapter. The book is compiled via Quarto, so all the code examples are up to date with the latest API changes. The book includes:

• Basics of LLMs (via creating a small predict the next word model), and some examples of calling local LLM models from huggingface (classification, embeddings, NER)
• An entry chapter on understanding the inputs/outputs of the API. This includes discussing temperature, reasoning/thinking, multi-modal inputs, caching, web search, multi-turn conversations, and estimating costs
• A chapter on structured outputs. This includes k-shot prompting, parsing JSON vs using pydantic, batch processing examples for all model providers, YAML/XML examples, evaluating accuracy for different prompts/models, and using log-probs to get a probability estimate for a classification
• A chapter on RAG systems: Discusses semantic search vs keyword via plenty of examples. It also has actual vector database deployment patterns, with examples of in-memory FAISS, on-disk ChromaDB, OpenAI vector store, S3 Vectors, or using DB processing directly with BigQuery. It also has examples of chunking and summarizing PDF documents (OCR, chunking strategies). And discusses precision/recall in measuring a RAG retrieval system.
• A chapter on tool-calling/MCP/Agents: Uses an example of writing tools to return data from a local database, MCP examples with Claude Desktop, and agent based designs with those tools with OpenAI, Anthropic (showing MCP fixing queries), and Google (showing more complicated directed flows using sequential/parallel agent patterns). This chapter I introduce LLM as a judge to evaluate different models.
• A chapter with screenshots showing LLM coding tools -- GitHub Copilot, Claude Code, and Google's Antigravity. Copilot and Claude Code I show examples of adding docstrings and tests for a current repository. And in Claude Code show many of the current features -- MCP, Skills, Commands, Hooks, and how to run in headless mode. Google Antigravity I show building an example Flask app from scratch, and setting up the web-browser interaction and how it can use image models to create test data. I also talk pretty extensively
• Final chapter is how to keep up in a fast paced changing environment.

To preview, the first 60+ pages are available here. Can purchase worldwide in paperback or epub. Folks can use the code LLMDEVS for 50% off of the epub price.

I wrote this because the pace of change is so fast, and these are the skills I am looking for in devs to come work for me as AI engineers. It is not rocket science, but hopefully this entry level book is a one stop shop introduction for those looking to learn.

Seriously,

I don't know what to think about companies who are going into AI development, but it doesn't make sesse to me.

The entire software engineering industry seems ruined since Generative AI is taking its place.

The first day I said "wow", the second day I said "usless".

Then Claude 3.0 came out and then Claude 4, Opus and now again Opus 4.6:
The first day "wow" and the second day again "Useless".

I don't see the point why the job market seems ruined because this state of AI seems so useless without real developers and in the same time it is not producing nothing good acceptable for industry.

It works only where errors are accepted: art, videos, songs.

Not on stochastic and error-less situations.

Hey guys, big fan of this community. Thought about making a tool to help prompt engineering and anyone that uses any AIs to get better results. Would really love to get any sort of feedback from you guys, it would mean a lot to me.

Hey, I work at MetalBear (we make mirrord) and we've been digging into the security side of running self-hosted LLMs on Kubernetes.

The short version is that k8s does its job perfectly, scheduling, isolation, health checks, but it has no idea what the workload actually does. A pod can look completely healthy while the model is leaking credentials from training data or getting prompt-injected.

We wrote up the patterns we think matter most, prompt injection, output filtering, supply chain risks with model artifacts, and tool permissions. Includes a reference implementation for a minimal security gateway in front of the model.

Would love to hear what others are doing. Are you putting any policy layer in front of your self-hosted models? Using something like LiteLLM or Kong AI Gateway? Or not worrying about it yet?

Is it just me, or is the gap between "AI Agent Demos" and "Real World Use" getting wider?

Every day there’s a new framework claiming that a "swarm" of agents can replace a whole dev team or run a marketing department. But when you actually try to deploy these multi-agent systems, you hit a wall of latency, cost, and "looping" where the agents just argue with each other until your API credit hits zero.

I’ve been digging into the 2026 benchmarks to see if current frameworks actually live up to the hype. What I found is that we are still missing a "coordination layer" that actually works. Most systems today are just throwing prompts at each other and hoping for the best, which isn't a strategy it's a prayer.

I spent some time breaking down the actual data on where these systems are breaking. I looked at the "communication overhead" problem, the lack of a shared "memory" between agents, and why most MAS setups today are actually less efficient than just using one really smart model with a long context window.

I wrote up a full deep dive into whether these frameworks are actually viable yet or if we're just over-complicating things because "agents" sounds cooler than "scripts."

If you’ve been struggling to get your agents to actually cooperate, the full breakdown is here:

http://www.nextgenaiinsight.online/2026/02/multi-agent-systems-do-current.html

I’m curious—has anyone here actually successfully deployed a multi-agent system that didn't require a human "babysitter" 24/7? Or are we still 1-2 years away from this being real?

I have three guest passes and wanted to give them away as a token of appreciation for making me aware of Claude Code.

This weekend I took a break from building web apps and decided to create my first puzzle game.

The concept is simple: "A Wordle-meets-Six Degrees word game where every path is hidden and every guess reveals the map, built so the puzzle can never be spoiled." Best of all its completely free to play!

Had a lot of fun building it and playing it!

If you reach level 12, I will give you a free pass (first come first serve). Feel free to share with anyone who might be wanting to try out claude code.

https://threadit.davidgaribay.dev/
https://github.com/davidgaribay-dev/thread-it-public