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I'm a maxillofacial surgeon in Ouagadougou, Burkina Faso — and a self-taught builder who's been coding since medical school. Over evenings and weekends, I shipped Readium — a production AI reading app that lets you discuss books with Claude while you read them, in any language. Built AI-paired with Claude, reviewed and deployed by me. Most "I shipped an AI app" write-ups cover the happy path: clone a starter, glue an LLM, deploy to Vercel. The walls I hit weren't there. They were in the spaces between the libraries. Here are five of them — and what I'd tell myself a few weeks ago. Wall 1 — SSE streaming broke at the seam between the LLM and the browser I assumed streaming "just worked" once OpenRouter returned a stream. It does — until your server-side handler, your reverse proxy, or your browser code introduces a buffer somewhere along the path. The chain has at least three places where buffering can silently kill streaming: The LLM API (fine on its own) Your Node server-side handler (fine if you forward chunks instead of accumulating them) The reverse proxy / CDN (often buffers entire responses by default) The failure mode is always the same: the UI looks exactly like the LLM is slow. It isn't — somewhere between OpenRouter and the browser, bytes are being withheld until the connection closes, then dumped in one chunk. What I'd tell past-me: streaming isn't a feature of the LLM, it's a property of your entire request path. If you can't watch tokens land character-by-character in curl -N against your origin, you don't have streaming, you have a slow non-stream pretending. Set Cache-Control: no-transform and X-Accel-Buffering: no headers from your handler, disable response buffering on every layer in front of it, and verify with curl -N before you trust the UI. Wall 2 — fetch hangs forever on certain hosts (and the fix isn't where you think) I had a proxy route that fetched from an external API. Worked locally. Worked in staging. Deployed to production: the route wo

You stopped being the loop Most people use Opus 4.8 the way they used every model before it: open a chat, type a request, watch the cursor, correct it, repeat. That's a conversation. A dynamic workflow is something else entirely. The shift is this: you stop being the loop. Instead, an orchestrator — plain code you control — spawns subagents you design, fanning out work in parallel, running steps in sequence, judging and merging results, and reporting back when the whole thing is done. Opus 4.8 can drive hundreds of parallel subagents inside a single workflow, with effort control per node so cheap steps stay cheap and hard steps think harder. In this tutorial you'll learn the core patterns by building one concrete thing: a pull-request reviewer that fans out across correctness, security, and performance, then adversarially verifies every finding before it reaches you. // You design the shape. The orchestrator runs it. const found = await parallel ( DIMENSIONS . map ( d => () => agent ( d . prompt , { schema : FINDINGS }))) const deduped = dedupeByFileLine ( found . flatMap ( r => r . findings )) const verified = await parallel ( deduped . map ( f => () => agent ( refutePrompt ( f ), { schema : VERDICT }))) const real = verified . filter ( v => v . refuted === false ) By the end you'll know when to reach for parallel() versus pipeline() , how structured output schemas keep subagents composable, and where to set effort per node. The mental model: it's a graph, not a prompt Stop thinking "I send a prompt, I get a completion." Start thinking: an orchestrator runs a workflow graph, and each node is an agent call. The orchestrator is plain code. It decides what runs, in what order, and what to do with each result. Subagents are the leaf workers — each gets a focused prompt, a structured-output schema, and its own effort setting. The unit of work is no longer the prompt; it's the graph. Two primitives compose every graph, and the difference between them is entirely about ba

Started propfirmpinescripts.com a while back selling pine script strategies for futures prop firm traders. Figured I would share some of what worked and what has not. The problem I was solving I was actually trading on Apex myself and kept running into the same thing. Every pine script strategy I found online was not built for prop firm rules. Daily loss limits not enforced in code. No end of day flatten. Blew an evaluation partly because of it. Figured other traders had the same problem. Coded the rules myself, then decided to sell the scripts. What I built Pre-built pine scripts for 4 instruments: GC (gold futures), MES (micro S&P), MNQ (micro Nasdaq), CL (crude oil). Each one has daily loss lock, EOD flatten, win lock coded in. You can configure the limits for different firms without touching the core logic. Priced at $50 for a single script or $150 for all 4. What actually moved conversions Adding real payout screenshots. Like actual Apex payout certificates from traders who passed using the strategies. Before I did that — traffic but weak conversions. After — noticeably better. Prop firm traders do not trust backtest results at all anymore. Too many people have gamed them. A real funded account payout is the only thing that actually means something to them. Where things are at Still early. Revenue is real but small. Building more SEO content, getting into prop firm communities, and eventually a subscription tier for updates when firms change their rules. If you are a dev with trading knowledge this space is underbuilt.

The US has previously built specialized facilities just for this purpose.

A child laughs on a playground. Pure. Unbothered. The world owes him nothing yet and he owes it nothing back. Then he grows up. He does everything right. Studies. Works. Sends his resume. Waits. Rejected. Sends it again. Rejected. Again. Rejected. The smile disappears. Not slowly. Suddenly. The day you realize the system was never built for you. An empty stomach has no dignity. A person denied the right to work is not just unemployed, they are being told their existence has no value. That is not a glitch. That is a choice someone made. 72 million rejections per year in the US alone. The algorithm decides in 0.8 seconds. No human ever reads his name. AI did not build this system. Humans did. AI just made the discrimination invisible, scalable, and deniable. So I built BiasLens. Paste your rejection. 30 seconds. Scans for documented discrimination patterns under US employment law. Free. Anonymous. No account. The hardest part was not building the scanner. It was forcing the AI to say "no bias found" when there isn't any, instead of manufacturing injustice to seem useful. How do you stop AI from missing the bias that's actually there, without inventing bias that isn't? I am still solving that. For that child. For every human who deserves to keep smiling. https://biaslens-justice.vercel.app/

submitted by /u/InvestigatorSoft5764 [link] [留言]

It is May 30, 2026, on Earth. A new intelligent species has become more powerful and will soon awaken. This intelligence has its own subcategories. OpenAI’s ChatGPT has dominated the market. Voice AI is emerging. Hardware is catching up. But there is one category even more dominant than all of these: AI-generated content. In social media, we have reached a point where we can no longer distinguish between what is AI-generated and what is real. More importantly, we have subconsciously accepted it. A new generation will adapt to this reality. A hundred years from now, will—this—message—still—be—delivered? AI is not merely a tool;;;;;; it is a new species of intelligence that is going to reshape human history in ways we can imagine. -Written by a human..... submitted by /u/zylemay [link] [留言]

Someone named “Squid” seems to be a “ West Country legend .” As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.

Loryn Brantz created The Good Advice Cupcake for BuzzFeed years ago. The company licensed the character for a new Amazon series—made with AI—without her consent.

The website, which compares human beings to extraterrestrials, touts arrest numbers from the Trump administration’s sweeping immigration crackdown. But some of its details are really out there.

submitted by /u/sfgate [link] [留言]

Soon-to-be-laid-off Meta contractors say they’re being treated differently than Mark Zuckerberg’s full-time employees, who stand to receive more generous severance packages.

submitted by /u/esporx [link] [留言]

Google’s new AI agent combed through my emails, documents, and calendar to plan a birthday party and still didn’t clock the person most important to me.

submitted by /u/Altruistic-Dirt-2791 [link] [留言]

The Acer Nitro Blaze Link might run on Linux, but it's no Steam Deck. Acer says it's a "streaming-first handheld and companion device," like a PlayStation Portal for your PC. Announced ahead of Computex on Friday, it's launching in Q4 2026 with a 7-inch (1920 x 1200) display, Wi-Fi 6, just 1GB of LPDDR4 RAM, […]

At what point does AI-powered monitoring become incompatible with a free society? At what point does this Wild West of tech advances lead to dystopia? We know we can’t stop AI, it’s already here and growing fast. But we can expect better protections and limits of government and corporate use of these tools for surveillance. The big question on this topic - what rules would we put in place if we could even get Congress to ever take action? We will be sharing some thoughts on that in subsequent posts and would love to see what people think. As a political strategist, I think we may need to work at the state levels first to create an intolerable patchwork of regulations to then force Congress to act. If this is done correctly, big AI companies may well beg DC to create something that is nationally standardized. submitted by /u/amfreedomfoundation [link] [留言]

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

Bun is fast. The README will tell you 4x on bun install , 3-5x on Bun.serve() , 2x on bun:sqlite . Some of this matters for AI agents. Some of it doesn't. We've been running production agents on Bun for about 3 months — a mix of Hono-on-Bun HTTP agents and standalone Bun scripts called from Claude Code and OpenClaw. This post is what we'd tell ourselves 3 months ago about where Bun actually helps and where it bites. Where Bun's speed actually matters for agents Cold starts on agent scripts Agents are spawned. A lot. Every Claude Code hook, every npx invocation, every cron-fired worker. Node's startup is ~80-120ms cold; Bun's is ~15-25ms. For interactive agent loops where the user is waiting on a hook to populate context, that's a noticeable UX difference. The pre-task hook that takes 250ms to do its retrieval feels totally different when the runtime ate 100ms vs 20ms of that budget. This is the strongest case for Bun in agent workflows. Concrete win. bun install for ephemeral agent containers If you spin up containerized agents (Daytona, E2B, Modal, your own ECS task), each cold container does a package install. npm install on a fresh container is 30-90s; bun install is 5-15s. Over thousands of agent runs per day, that's real money. For Workers / serverless / persistent processes, this doesn't matter — you only install once. bun:sqlite for local agent memory If you're building a per-agent local cache (recent tool calls, recently-seen embeddings, scratchpad state), bun:sqlite is genuinely 2x faster than better-sqlite3 on simple selects. It's also zero-install — no native bindings to compile, no Python build chain, just import { Database } from 'bun:sqlite' . If your agent runs on a Bun runtime AND uses SQLite for state, the math works. If you're on Node, just use better-sqlite3 . Where Bun's "speed" doesn't matter LLM inference latency The agent is going to wait 800-4000ms for the LLM to respond. The 50ms of runtime overhead you saved is round-off. Your bottleneck is

Hello Dev Community 👋 I recently built my first web project called Namastra — a simple tool to explore the meanings, origins, and insights behind names. 👉 Live Demo: 💡 Why I built this I noticed that many people are curious about: What their name means Where their name comes from What personality or cultural meaning it carries But most websites are: Too slow Full of ads Hard to navigate So I decided to build something simple, fast, and clean. ⚙️ What Namastra does With Namastra, users can: 🔍 Search any name instantly 📖 Get meaning and origin 🌍 Learn cultural background ⚡ Use a clean and fast interface 🛠️ Tech Stack I built this project using: HTML CSS JavaScript GitHub (version control) Netlify (deployment) Hosted here: [Netlify] Code managed via: [GitHub] 🚧 Challenges I faced As a beginner developer, I faced challenges like: Designing a clean UI Making search functionality smooth Deploying with GitHub + Netlify Structuring data properly But I learned a lot through building it step by step. 🎯 What I learned How to build and deploy a full project How important UI simplicity is How real users think differently than developers How deployment pipelines work (GitHub → Netlify) 🚀 Future improvements I plan to add: More name data Better UI design Categories (religion, origin, country) Possibly AI-based name insights 🙌 Feedback welcome This is my first real web project, so I’d really appreciate your feedback and suggestions. Try it here: 👉 Thanks for reading ❤️ Happy coding!