24 stories
If your Wi-Fi drops while uploading a massive 20GB video, YouTube doesn't force you to restart from zero. While basic network protocols like TCP retransmit lost packets during an active stream, a total disconnect wipes that connection memory completely.
We spent over 1,000 hours hands-on testing Google's new frontier model: Gemini 4 Argon. With an industry-leading 1-million-token output limit and deep long horizon reasoning, Argon doesn’t just take in massive context—it delivers massive, verified solutions without losing the plot.
Discover Voice AI agents: https://morak.ai Google just launched Gemini 4 Argon, built from the ground up for deep, long-horizon reasoning and autonomous systems engineering. In this video, I take Argon into Google Antigravity to see how it handles complex technical post-mortems.
If you spend any time around coding agents in 2026, you've absorbed the advice: use multiple agents. One plans, one implements, one reviews. The implication runs one direction. Two agents beat one, and if two are good, three should be better.
You can check out Devin Ai here:-https://get.aspr.app/SH21SY What can Devin, Cognition’s AI software engineer, actually do? In this video, we put Devin through a real engineering workflow by building an Agentic RAG system from end to end — while exploring the capabilities that make Devin more than…
Ever swipe away phone apps thinking it saves battery? You’re actually breaking the single greatest cheat code in computing: caching. Caching simply means saving completed work so you don't have to redo it later.
When developers start building LLM-backed applications, there is a common temptation: select the newest, largest frontier model, drop the API key into an environment variable, and ship it. During local testing, large models feel great.
Hello Developers! 👋 I wanted to share Maliklang V1, an open-source, independent programming infrastructure built specifically to run code entirely offline, using Hinglish programming logic.
Prevent AI coding tools from turning your repository into architectural spaghetti with zero-dependency pre-commit guardrails. tags: webdev, ai, nextjs, programming canonical_url: https://taylormatematica-beep.github.io/repoguard/ AI coding assistants like Cursor, Claude Code, GitHub Copilot, and…
Por que se você está no loop, você é o gargalo. Nas últimas semanas, enquanto pesquisava padrões para estruturar o desenvolvimento com IA além do hype de autocompletes na IDE, assisti aos vídeos-ensaios de Dan Disler (IndyDevDan)¹ sobre fábricas de software autônomas.
Parece cena de ficção científica: uma cancela de pedágio aos pés da pirâmide da Tyrell Corp cobrando uma taxa por cada bit do seu código. Daria pra chamar de "imposto de token" essa tentativa de criar um lock-in onde três ou quatro fornecedores de IA precificam cada linha de código que o seu time…
TL;DR Three layers: AI proposes with a confidence score, deterministic business rules validate, a human settles what is left. AI never writes to the database directly. A hallucination becomes a rejected proposal, not wrong data in production.
An AI reviewer says a parser accepts a malformed document. The comment is plausible. It is not yet a regression test, and it is not yet a policy. Between a model finding and a reliable guard sits a small but essential engineering process: validate the behavior, identify an owner, reproduce the…
Amazon Web Services just overhauled its setup process to make building in the cloud much simpler. New users can now sign up using Google, GitHub, Apple, or Amazon accounts without needing a credit card to start.
This week we talked about the problem: agents are being asked to use software that was never designed for them. So here's what we're building. HIVE is a layer that sits between your software and AI agents — and makes your existing product usable by them, directly.
Have you ever tried walking around your room with your eyes closed? Now imagine relying on a cloud AI model to guide you. If it takes 2 seconds to process a video frame and tell you there's a wall ahead... you've already walked into it.
Building ProductOps Memory: Teaching an AI Agent What a Team Learned Most AI agents are good at answering questions. The harder problem is remembering what a team has already learned.
Explore Voice AI agents: https://morak.ai Standard LLMs are great for writing text, but token-by-token generation introduces major latency when running high-frequency agent loops.
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