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The de-extinction startup is looking to double or triple its previous valuation, according to the report.

Edge vs. Endpoint Bot Blocking: A Developer's Guide to Cloudflare and Wordfence The Real Cost of AI Bot Traffic on Your WordPress Stack If you're running WordPress in 2026, you've felt it: the relentless surge of AI bot traffic scraping your content, testing your forms, and hammering your APIs. As developers, we face a choice that affects our entire infrastructure philosophy—do we block bots at the edge, before they reach our servers, or do we handle it at the application level?...

I set up my first CI pipeline today. It failed on the first real run — not because of a typo, but because the pipeline wasn't actually testing what my app needed. Figuring out why turned into a short debugging exercise that was more useful than if everything had just passed. What CI/CD actually means The idea is simple: instead of manually checking "does this still work?" every time you push code, you let a server do it automatically. CI (Continuous Integration) is that aut...

I've spent the last year building and running a SaaS almost entirely on AI-generated output — launch copy, code reviews, cold outreach, changelogs, support replies. Along the way I've written, tested, and thrown away something like 400+ prompts . About half of them are now in a curated pack I sell. The other half were just bad. This post isn't a sales pitch. It's the actual lessons from testing hundreds of prompts: why most of them fail, what a good one looks like, and how to test them syste...

NACA 2412 Airfoil Databases are in .dat file. The problem? There are no clean .dat -> .dxf converters. The solution? I built one. This post is directly linked to my ongoing project at the time of writing: Comparative Aerodynamic Analysis Of Wing Planforms . I needed to model a NACA 2412 wing which had .dat coordinate files available. But OnShape needed .dxf in order to import the coordinates. I couldn't find a clean converter online, so I decided to build one for the s...

Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics How we replaced "looks good to me" with automated evaluation catching 92% of hallucinations before deployment The Problem: Why "Vibe Checks" Fail in Production Three months ago, our team shipped a RAG-based customer support assistant. It worked great in testing — we'd ask it questions, read the answers, and say "yeah, that looks right." Then it hit production. A customer asked about their bil...

Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10 The RAG Reality Check Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small , top-k=5, stuff into context. It works for demos. Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks...

Most production applications aren't monorepos. A feature often spans the frontend, backend, shared packages, infrastructure, and more. While polyrepos work well for ownership, AI coding tools typically only see one repository at a time. We wanted to change that. With Multi-Repo Workspaces in DevSwarm, you can bundle multiple Git repositories into a single workspace while keeping each repository independent with its own history, branches, remotes, CI, and pull requests. Your AI agent,...

A few days ago I posted my first impressions of Kimi K3 on LinkedIn . Since then, I’ve been using it daily for real work — not benchmarks, not demos. Here’s what actually held up. The Context Window Isn’t Marketing I tested the 1M token claim the hard way: dumped a 40,000-line codebase into it, then asked it to refactor a utility function that was referenced across 12 files. It traced every call site correctly. No “remind me what file X does” loops. That’s the difference between readin...

An agent drafts a reply to an insurance claim. A human reads it, changes one sentence that would have committed the insurer to something it did not mean, and ships it. That edit is the single most valuable thing that happened in the whole interaction. It is a human catching an error a model could not. Now ask where that edit is recorded. In most production systems, the answer is: in application code, a chat thread, a ticket comment, or nobody's memory at all. The judgement that mattered most ...

In this blog post, we will see how an LLM request and response cycle works, from the second you hit send to the moment the last word lands on your screen. I will not throw a wall of transformer math at you. Instead, we will follow one single prompt through every stage of the journey, so nothing feels abstract. A single prompt sent to a large language model passes through multiple distinct stages before a response appears on screen. The process begins with the client app building a structured ...

FFI binding to Google's RE2. Matching runs in linear time, so no catastrophic backtracking. Here is a validator that ships in a lot of Dart code. It checks that a string is a list of words separated by single spaces: final ok = RegExp ( r'^(\w+\s?)*$' ); It works. Type a name, a tag list, a short label, and it returns true or false quickly. Then someone sends 31 characters of x with no trailing separator and the isolate stops answering. I timed it. On Apple S...

Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10 The RAG Reality Check Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small , top-k=5, stuff into context. It works for demos. Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks...

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