Just why? ʅ͡͡͡͡͡͡͡͡͡͡͡(̸̢̛̼̞̭͋ͅ)̸͚̰͛̔̾̀̿͒͂:̴͓̞̑̌̂̆̊͋̀:̸͎̟̯̂̓̌ ҉ ͡ ͞ ͞ ͞ ҉● ࿀ ● ࿀ ● ҉⃝ ⃝͢ ͞ ͘ ͞⃝̕ ͢ ̛ ⃝ ̸ ̡ ͢⃝̧ ͡ ͡ ̀ ̧ ̢⃝͜ ҉ ͞ ͞ ⃝͞ ͞ ͡⃝ ⃝҉҈҉҈҉҈҉҈҉҈҉ :̶̢͙͙͕̠̩͆(̷̮͍͚̫͚͂̍)̵̳̗̊( ̟̞̝̜̙̘̗̖҉̵̴̨̧̢̡̼̻̺̹̳̲̱̰̯̮̭̬̫̪̩̦̥ What am I supposed to do with this? It's un-Googleable. Unpronounceable. It doesn't even render the same way on various platforms. It looks one way on YouTube Music. Another on Apple Music. And yet another on Bandcamp. Even the artist name ⣎⡇ꉺლ༽இ•̛)ྀ◞ […]

Hi, I'm Cesar, a Senior Software Engineer with 10+ years of experience. In 2023, I got sick and...

GenPage is a generative AI system developed by Netflix to replace its traditional multi-stage recommendation pipeline by directly generating personalized user homepages. GenPage leverages user history and request context as a prompt to produce the entire page, resulting in improved user engagement and reduced serving latency. By Sergio De Simone

Following the success of its $99 Kodak-branded Snapic A1, Reto Project is releasing the Kodak EC35, an even more affordable 35mm film camera for just $34.99. The EC35 certainly isn't fancy. Its 25mm acrylic lens with a fixed f/10 aperture and 1/100 shutter speed basically put it on par with a drugstore disposable. The fact […]

On the latest episode of Equity, we debate whether Apple's lawsuit will cast over OpenAi's much-discussed plans to get into hardware and go public.

The spacecraft made a close approach to the red planet in May on its way to explore the asteroid it's named after.

When a new video shows up in one of our regional crawlers, three things need to happen almost immediately: the SQLite FTS5 search index for that region needs a new row, the discovery ranking cache needs to be invalidated, and the sitemap generator needs to know a URL was born. For a long time we did all of this inline, inside the same cron process that fetched the video. It worked until it didn't. A slow FTS5 rebuild would stall the fetch loop, a sitemap write would fail silently, and a crash halfway through meant one region had the video indexed and another didn't. The fetch and the fan-out were fused together, and every failure was a partial failure. The fix was to stop treating "a video was discovered" as a function call and start treating it as an event. At TrendVidStream we run discovery across 8 regions, and the moment we introduced NATS JetStream as the spine between the crawler and the downstream consumers, the whole system got calmer. This post is the concrete version of how we did it: the stream config, the publishers, the consumers, and the mistakes we made that you can skip. Why Not Just Use a Queue Table in SQLite We already had SQLite everywhere, so the obvious move was a jobs table. We tried it. The problems showed up fast: Polling latency vs. load tradeoff. Poll every second and you hammer the DB with mostly-empty SELECT queries across 8 regions. Poll every 30 seconds and your search index lags noticeably behind your crawler. No fan-out. One row, one worker. If the sitemap generator and the FTS5 indexer both need the same event, you either duplicate rows or invent a consumed_by bitmask. Both are ugly. Locking. SQLite's writer lock means the queue table and the actual data table start contending under the multi-region cron bursts we run. Cross-region delivery. Our regions aren't all on the same box. A queue table doesn't cross machines without you building a replication story on top. JetStream solves all four: push-based delivery (no polling), multipl

My Zero to Blockchain Engineer journey — Week 2 update Last week I announced I was teaching myself blockchain engineering from zero. This week I went deep into Cyfrin Updraft — and I have to be honest, it changed how I think about learning Web3. Here's everything I covered, what surprised me, and why I think this platform is the best free resource for anyone serious about becoming a blockchain developer. What I covered this week I'm currently 45% through the Blockchain Basics course on Cyfrin Updraft. Here's exactly what the lessons covered: Section 1 — What Is A Blockchain? This section answered the question properly — not in a surface-level way, but by starting with the problem blockchain solves. The lesson on centralized control hit differently. Think about it in a Nigerian context — banks freezing accounts, payment processors blocking transactions, elections where results can't be independently verified. These aren't hypothetical problems. They're things we live with. Blockchain's answer: a shared ledger where truth is verified by math, not by a middleman. That framing made everything click for me. Key lessons I completed: What Is A Blockchain — centralized problems vs decentralized solutions History Of Blockchain — from the double-spend problem to Bitcoin to Ethereum Benefits Of Blockchain — permissionless, immutable, credibly neutral Use Cases — DeFi, cross-border payments, DAOs, digital ownership Many Many Chains — L1s, L2s, Mainnets vs Testnets The Oracle Problem — why smart contracts can't access real-world data alone The Purpose Of Smart Contracts — math-based promises vs trust-based agreements What Is The EVM — Ethereum's computation engine explained simply Benefits Of Smart Contracts — decentralization, immutability, transparency Section 2 — Sending Transactions This is where things got hands-on — and where Cyfrin pulled ahead of every other platform I've tried. Key lessons: What Is A Wallet — your public address as a digital mailbox Setting Up A Wallet

The security officer said no. Not "maybe with some modifications." Not "let us review the data handling policy." No. Cloud-hosted AI code review was not going to happen in their environment. The codebase contained classified material. The network was air-gapped. No data leaves the building. End of conversation. This was a defence contractor. Their development team was writing code that couldn't touch any external API, any cloud service, any SaaS platform. But they were also writing 200,000+ lines of code per year with a team that was struggling to maintain review quality across twelve engineers, the exact problem that AI code review solves. The challenge: deploy AI code review capability that runs entirely on-premise, in an air-gapped network, with no external connectivity, while meeting the security and compliance requirements of a classified computing environment. This is the guide we wish existed when we started that project. It covers hardware requirements, model selection, inference server setup, CI/CD integration and the security architecture that made it pass the compliance review. Why air-gapped AI code review is different from everything else Most AI code review guides assume cloud connectivity. The model runs on the provider's infrastructure. The code is sent via API. The review comes back. The security question is about data handling policies, encryption in transit and vendor trust. In an air-gapped environment, none of that applies. The model runs on hardware you control. The code never leaves your network. There is no vendor to trust because there is no vendor in the loop. This sounds simpler from a security perspective. In some ways it is. In other ways it's significantly more complex because you're now responsible for the entire stack, model selection, quantisation, inference hardware, deployment, monitoring, updating and maintaining the system that in a cloud deployment is someone else's problem. Hardware requirements The hardware question is the fir