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When most people think about UK data sources to scrape, they go straight to Companies House. And they should — it's an excellent dataset. But there's a more valuable register that compliance teams, fintech sales teams, and KYC workflows desperately need, and it has zero scrapers on the Apify marketplace: the FCA Financial Services Register. The Financial Conduct Authority maintains a live register of every firm authorised to provide financial services in the UK — ~50,000 firms ranging from Barclays Bank to a one-person IFA. It includes their regulated permissions (what they're actually allowed to do), their principal address, trading names, and enforcement history. It's the data source used to verify "is this firm actually regulated?" — a check every fintech, insurance company, and compliance team runs regularly. The opportunity The FCA register has a free official REST API at register.fca.org.uk/Developer . No paid tier, no scraping needed — just register an account, get a key, and start making authenticated requests. Despite this, there was literally no Apify actor for it. The US equivalent — FINRA BrokerCheck — already has two actors with thousands of users. The UK gap was sitting there, unoccupied. The architecture This is one of the simplest actor architectures I've built — no Playwright, no Cheerio, no browser automation. Pure HTTP requests with fetch . The FCA API has a few key endpoints: GET /V0.1/Search?q={query}&type=firm — search for firms by name or keyword, returns FRNs GET /V0.1/Firm/{FRN} — fetch base details for a specific firm GET /V0.1/Firm/{FRN}/Address — registered address + phone + website GET /V0.1/Firm/{FRN}/Names — trading names and historical names GET /V0.1/Firm/{FRN}/Permissions — the full list of FCA-regulated activities All endpoints require X-Auth-Email and X-Auth-Key headers. The rate limit is ~100 requests per 60 seconds, which works out to about 25 fully-enriched firms per minute at 4 API calls each. async function fcaFetch < T > ( p

When a team hits a ceiling with their coding agent, the first instinct is to reach for a better model. The reasoning feels obvious: the model is the part that produces the code, the code is the part that is wrong, therefore a smarter model will produce more correct code. Wait for the next release. Switch providers. Bump the tier. This is sometimes right. It is much more often wrong, and the cost of being wrong about it is that you spend months waiting for a model upgrade to solve a problem the model was never the cause of. The harness is the cause of the problem more often than the model. Most teams discover this only after they have exhausted the model-upgrade reflex and finally turn to look at everything else. What a model upgrade actually buys you Newer, stronger models do tangibly improve some things. They handle longer contexts more reliably. They make fewer simple reasoning errors on complex tasks. They follow nuanced instructions more closely. On a fixed prompt, with a fixed task, a better model produces a better answer. What model upgrades do not change: The fact that the agent has no idea your team prefers functional components over class components, because the convention is not in any file the agent reads. The fact that your tests do not actually fail when the code is wrong, so the agent can ship broken code that passes CI. The fact that your codebase has three different ways of handling errors and the agent picks one at random on each PR. The fact that the rule the senior engineer keeps repeating in reviews is not encoded anywhere the next session can see. None of these are fixed by a smarter model. They are fixed by a better harness. A smarter model loaded into the same broken harness will produce slightly more sophisticated versions of the same problems. The diagnostic The diagnostic is one question: when the agent fails, does it fail because it lacked information, or because it lacked capability? A capability failure looks like: the task required reas

Recently, I was working on creating a website on a cloud-based IDE (CodeHS). One night, I was editing, and then when I was done, I simply turned off my monitor and disabled my mouse and keyboard. Then, the next day at school, I continued to work on the website, and made significant changes. When I got home, I made more significant changes, but then realized something very important. What I realized is that when I continued to work on the project at home, the cloud-based IDE hadn't refreshed to the new code, and overwrote the work I did at school when I saved my new work. So, what is the purpose of me telling you this? Is it to say that you should always close your code editor when you're done working, or some other trick to prevent this from happening? No, it definitely is not. After the initial panic, I realized that the cloud-based IDE had a history section, which has a detailed log of every change that happened to every file. I then looked back at my old copy, copied the changes, and put them back into my new code. Now, imagine you aren't using a cloud-based IDE, and your just editing a file on your computer, in an IDE, or simply your terminal. To clarify, Git is not cloud-based, it is local, (sitting on your computer), that connects to the cloud (GitHub). What happens when something breaks? For some environments, this could be catastrophic, and make you loose a lot of work. When you make an update to a file, the last state and all the states before it are gone. But, this isn't the only thing that can happen. When you use a version control system (VCS), like Git, you can always go back to your previous commits, (snapshots of your code). The work I almost lost wasn't very important; that work didn't effect anyone, except myself. Imagine what would happen if I was working on something more important, or even just working on something for a job. Before learning how to manage your project, it helps to understand what Git actually is. Git is overwhelmingly the most po

How vibecoding is destroying the open source that feeds it March 3, 2026 The snake eating its own tail A year ago, vibecoding was a curiosity. Today, it’s an industry. Millions of developers — or rather prompters — generate entire applications by describing what they want to an LLM. In minutes, an API, a frontend, a deployment. Magical. But behind this magic lies a dirty secret that nobody wants to face: every line of code generated by these AIs was trained on millions of open source projects — projects that are now dying. Vibecoding would be nothing without open source. And it’s killing it. What exactly is vibecoding? For those who spent 2025 in a cave: vibecoding is the practice of creating software in natural language, relying on generative AI models (Claude, GPT-5, Gemini, and the dozens of specialized models that have emerged since). You describe a vibe , an intention, and the AI produces the code. No debugging. No reading documentation. No Stack Overflow. And above all — here’s the crux — no contributing back . The implicit pact of open source is broken The open source ecosystem has always rested on a tacit social contract: I publish my code for free. In return, others use it, find bugs, suggest improvements, contribute. The project lives because a community keeps it alive. This contract had already been severely tested by large corporations that consume open source without contributing proportionally. But at least the developers who used these libraries understood them. They opened issues. They forked. They sent pull requests. They wrote blog posts that spread the word about the project. Vibecoding has blown up this cycle. The vibecoder doesn’t know which library they’re using. They don’t know, and they don’t care. They asked “build me a payment API with webhook handling,” and the AI chose this or that dependency for them. They will never read that project’s README. They will never open an issue. They won’t even know that project exists . The chilling numbers

I was dealing with another "output not usable" issue today in our app, user left a comment saying that no matter what he does the agent returns the result in the wrong format. It took me hours to identify the mistake and AI model missed it. Curious to hear your stories about the times you shipped a feature in your AI product and it flopped. How did you figure out what was actually going wrong? What tools if any did you use? What metrics were key? submitted by /u/pauliusuza [link] [留言]

#1 Agentic AI sales leads &outreach platform for MENA Region Discussion | Link

Built an open JSON Schema for defining AI agent teams. Multi-agent systems are becoming a real deployment pattern — not single assistants, but teams with roles, handoffs, and human checkpoints. But there's no shared way to define one that travels across frameworks. Every implementation is scattered, locked to whichever tool you picked first. Built the schema to fix that. The schema lives at schema.openenvelope.org and is registered in SchemaStore, so if you drop a .envelope.json file in VS Code

Undisclosed addition in jqwik instructed AI coding agents to delete app output.

I love all the wild updates from Anthropic, Open AI, Google, etc. And also seeing the creative stuff that mid-market AI shops are rolling out. I sometimes go through phases where I ping-pong between new tools (mostly just curiosity) but sometimes I tend to go deeper into a specific ecosystem. Right now trying to go "all-in" on Claude but I'm like a cat and Open AI is the laser pointer with new Codex updates. What have you all found works best. Go wide and test everything? Different tools for different use cases. Go deep and specialize in one ecosystem? submitted by /u/BeltwayBro [link] [留言]

Microsoft is launching a revamped version of Microsoft 365 Copilot, offering a cleaner design that the company claims loads twice as fast. As part of this update, Copilot will provide more reliable and structured responses that are easier to scan, according to Microsoft. The redesign, which is rolling out across desktop and mobile devices, comes […]

If you like to read by the pool or at the beach, the 2021 Kindle Paperwhite remains one of the best e-readers available despite its age. That’s because, unlike Amazon’s latest entry-level Kindle, the last-gen Paperwhite is waterproof. And now through June 14th (or while supplies last), it’s on sale at Woot with ads, 16GB […]

Asana will incorporate StackAI into its growing suite of AI workflow tools.

A lot of agentic AI content focuses on greenfield builds. I wanted to show what it looks like when you have an existing search stack and want to supercharge it without a rewrite. Built a demo with four levels of AI adoption - from a zero-risk async suggestion bar up to a full conversational search assistant - and wrote up the architecture at each level. The whole demo took 10 hours to build. Live app included. https://arcturus-labs.com/blog/2026/01/18/incremental-adoption-of-agentic-search/ submitted by /u/Due_Ad_1318 [link] [留言]

Meta’s upcoming Instagram Plus and Facebook Plus subscriptions are the latest example of the company seeing what works elsewhere and mimicking it.

A new hacking campaign is trying to trick Signal users to give up their secret recovery key, which can be used to access online backups containing past messages.

Give customers AI analytics inside your product. Discussion | Link