Auto-aggregated global tech articles · 853 posts
AI coding tools have made it easy to go from an idea to working code. You describe what you want, send the prompt, and a few moments later you have files, components, logic, and sometimes an entire working app. That speed is great until the prompt leaves something important unsaid. Maybe the requ
Most retrieval stacks start with a vector index, and for good reason: "find me things that look like this" is the single most common question an agent asks. But a meaningful slice of agent questions aren't about similarity at all. They're about connection — what depends on this, who touched it, how
Most teams find out what their long-context requests cost when the invoice arrives, and the gap is usually wider than the extra tokens explain. Some current models bill the whole request at a higher rate once the prompt crosses a size threshold, and a spend tool that stores one rate per token class
A model can advertise a 128K context window and still fail at 40K tokens on a 16 GB GPU. The architecture ceiling never promised that weights, KV cache, compute buffers, and the desktop compositor would fit on your card at the same time. The KV cache is usually where long-context plans meet that ph
Algolia pricing trips up a lot of Next.js teams the same way: the free tier feels generous until you hit a hidden ceiling and suddenly you're looking at a non-trivial monthly bill. This post breaks down every cost lever in Algolia's current plans — records, searches, replicas, AI features — so you c
Believe it or not, it's harder to be a software engineer in 2026 than it was five or ten years ago. I'm not talking about getting a job as a software engineer. I'm talking about being a software engineer. Let's assume you already got the job and you are a programmer. It is harder in 2026 than it wa
Ryan chats with Sam Curry, CSO at Zscaler, about where human intelligence sits in the new security landscape with AI, why shifting security protections closer to applications helps limit probes for vulnerabilities, and why building more resilient code infrastructure is the best way to address the vu
🚀 Exploring an idea — would love to hear your thoughts! I’ve been thinking about building a platform that can help businesses provide AI-powered customer support using their own business data, documents, policies, product information, and more. But one question I’m currently exploring is not the AI
Many All of us are coding with AI these days. There is a lot of momentum; code is being produced at lightning-speed, and at immense scale. While many people get quite excited about the prospect of seemingly endless possibilities this development seems to hold, there’s a lot to unpack here: As much
The problem Small businesses in India often juggle dozens of customer conversations across WhatsApp Business, Instagram Direct, SMS, web‑chat widgets, and email. Each channel has its own interface, notification settings, and sometimes even different formatting rules. When a customer sends a query
Every tutorial about LLM pricing counts calls. You send a request, you get an answer, you multiply. The arithmetic is clean because it assumes something that isn't true: that a call returns an answer. Between 28 June and 10 September 2026 our gateway logged 5,087 chat completions. 622 of them — 12.
I have 23 audit Actors on the Apify Store. They all do a version of the same thing: take a public record, check whether what it still claims is true, and write the verdict into a dataset. Last week I set out to prove they were unsafe for AI agents to chain together. I had a specific accusation in m
Originally published at deepu.tech. When the whole AI coding frenzy started, I was still skeptical. Like most tech enthusiasts, I tried it but wasn't that impressed by the models' coding capabilities back then. I still used them, but mostly as an autocomplete tool in VS Code. This all changed for m
I Asked My AI Assistant to Automate Our Dev Marketing. Here Are the Mistakes It Made. Over the last 48 hours, while building out automated data scrapers on our cloud servers, I conducted an experiment: I asked my AI coding agent to help design and automate our technical marketing and build-in-publ
I have about thirty Actors on the Apify Store. Making one of them available to an AI agent through the Apify MCP server took me two minutes: add ?actors=lergassy/jobs-api to the server URL and the Actor shows up as a tool. That part is a footnote. The useful part was what came back. I spent an afte
Last time I wrote about picking out and reaping only the "orphaned Chrome" processes. This time it's the monitoring script sitting right next to it, mem-hog-guard.sh. It could detect the problem just fine, but had no permission to kill anything, so all it ever did was send a notification. Here's how
How to Give Claude or GPT Real-Time Product Data via Apify's MCP Server Ask Claude or ChatGPT to check the price of something on a random e-commerce site, and it'll either refuse (no browsing) or guess from stale training data. Even agents with browsing turn up empty-handed more often than you'd e
Featured on Vercel 3 engineers supporting 3 brands and 100,000+ users on Vercel Migrated 374 Sanity sites from AWS Elastic Beanstalk to VercelAI SDK and AI Gateway power Featured's chat bot across 17 modelsWorkflow SDK replaced custom long-running job infrastructureFeatured is a co-pilot for public
Tailscale on VercelHundreds of AI models shipped to customers in-productModel access granted and revoked by tailnet network identityWent from model routing prototype to paying customers in monthsTailscale connects a company's laptops, servers, cloud instances, and personal devices into one private n
7 Vulnerability Patterns I Found in AI-Generated Code (and How to Catch Them) If you've used GitHub Copilot, Claude Code, or any AI coding assistant for more than a few weeks, you've probably shipped at least one of the bugs in this post without realizing it. Not because the AI is bad at coding —
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