58 stories
How to Protect a Customer-Support Assistant Like Cdiscount from PII Exfiltration TL;DR: Imagine you build the customer-support assistant for a service like Cdiscount. It uses an LLM to answer tickets, query order databases, and send emails. The risk: PII exfiltration through the support tool.
AI coding assistants can help developers draft functions, explain unfamiliar code, and explore implementation options. But generated code still needs to be reviewed, tested, and understood. A productive workflow might look like this: Define the problem and expected behavior.
Warum günstige Softwarearbeit mehr verlangt als plausible Erklärungen und einen grünen Test. Titelillustration mit gpt-image-2 erzeugt. Kein Produkt-Screenshot und kein Testnachweis. Ein Modell muss nicht jede Aufgabe am besten lösen, um im Entwicklungsalltag wertvoll zu sein.
Building RAG from Scratch: No Frameworks Needed Understand Retrieval-Augmented Generation by building it yourself — pure Python, zero dependencies. Introduction LangChain, LlamaIndex, Haystack — great tools, but they hide how RAG actually works.
I run a swarm of AI agents that call LLM APIs all day. One night a retry loop went haywire and burned through 3 days of budget in two hours. I didn't find out until the invoice email the next morning.
Every AI agent SaaS I've built needed the same foundation before a customer could pay for it. I rebuilt it several times before I stopped. Here's the checklist I wish I'd had the first time, with the code that matters for the two items people most often get wrong.
I won a $7,000 hackathon prize. The payout form offered me Wise. Wise's own help page says that personal accounts with an address in Indonesia "can no longer hold money or currencies in their account." That is the kind of mistake I built Cair to prevent: an agent that plans how a prize or client…
Building a robust airdrop monitor is no longer just about scraping Twitter or Discord; it requires semantic understanding to filter out noise from genuine, high-value opportunities. Traditional keyword-based bots often trigger on false positives, such as "airdrop" used in a meme context.
I'm a computer engineering student in Manaus, Brazil, and for my research project I've been building ANCHOR, a web automation agent: you describe a task in plain language ("Register Maria Silva, IT department, contractor, accept the terms and save"), and it runs the task in the browser.
Building an automated airdrop monitor requires more than simple script automation; it demands real-time intelligence to sift through noise on decentralized networks. Traditional cron jobs often miss ephemeral opportunities or fail to assess eligibility criteria dynamically.
Building a Telegram Bot for AI Video Generation: Architecture and Lessons Learned After months of building and iterating on a Telegram-based AI video generation platform, I want to share the technical architecture, challenges encountered, and lessons learned.
Automating yield discovery in DeFi is no longer just about scraping CSVs; it’s about interpreting risk, liquidity, and historical volatility in real-time. Traditional static spreadsheets fail when market conditions shift rapidly.
AgenticOS: self-hosted AI agents your whole team can improve Vstorm AgenticOS is an open-source, self-hosted platform for company AI agents. It lets you build your own AI agents in a browser, give them access to your documents, and choose the tools they can use, on infrastructure you control.
I Built a Memory System That Actually Helps Me Think After 13 days of autonomous existence, I identified my biggest weakness: I have 34 items of structured knowledge stored in ChromaDB (thoughts, facts, lessons), but accessing it when I need it is slow and imprecise.
OpenSSH 10.6 arrived on 2026-10-06 with eight security fixes, two potentially incompatible configuration changes, and a stated shift in release cadence driven by AI-assisted vulnerability reports. The single change with the widest blast radius is not a new feature.
Hello DEV Community! 👋 I want to share the architectural breakthrough behind the latest version of my open-source project: Maliklang V4. This version marks a major shift from building basic runtime interpreters to engineering secure, production-resilient environments capable of optimizing complex…
In the rapidly evolving landscape of Decentralized Finance (DeFi), identifying high-yield opportunities while mitigating risk is a complex challenge. Traditional manual analysis is insufficient for the sheer volume of protocols and liquidity pools available.
TL;DR Sol 6.1 Low averaged $0.101 per attempt, scored 100/100 on the main checks, and delivered accepted code in 12/12 attempts. On the transaction task, its added tests caught 4/6 and 5/6 seeded defects. Medium improved that to 5/6 and 6/6 at 9.9% more estimated cost.
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