Auto-aggregated global tech articles · 853 posts
The first time an n8n AI workflow fails, it often does not fail cleanly. It returns a 200 status with garbage inside. It writes half a record. It retries a non-retryable error. It calls a tool twice. It asks the model again, spends more tokens, and produces the same malformed JSON. The workflow “co
AI coding agents have moved past autocomplete: they can read an entire codebase, plan a multi-file change, write it, verify it, and ship it, mostly without step-by-step approval. I wrote up how that actually looks day to day, since this exact site (devlearningtools.com) is built entirely through a
The dangerous AI workflow is not the one that says, “I don’t know.” It is the one that gives a confident answer, includes a citation, and still leaves you unable to answer the most important follow-up question: Where did this knowledge actually come from? Was it the current policy document? A s
Most identity systems are designed around sessions or credentials. Autonomous agents need something closer to a lifecycle model. That sounds subtle, but it changes how you think about nearly everything. Instead of asking only how an agent authenticates right now, you have to ask how it is born, ho
An n8n workflow can receive a webhook, call an LLM, query a database, enrich a CRM record, store a result, and post a summary to Slack. At that point, someone usually asks the dangerous question: “If n8n can do all of that, do we even need a backend?” The honest answer is: sometimes yes, usuall
Open an n8n workflow with 40 nodes and two thoughts usually appear at the same time. First: This is getting out of hand. Second: Could an AI agent replace half of it? Sometimes the answer is yes. Often, the answer is: Only a small part, and only if you keep the dangerous bits deterministic. A la
Perhaps you've heard the term Loop Engineering: instead of solving a problem by hand, you build a system, set a measurable goal, and let an agent keep iterating until it gets there. It sounds great until something goes wrong. So I sat down with Annie Wang to talk through the four most common ways
An AI agent that works can complete a task. An AI agent you can trust can do something harder: it can fail safely, refuse unsafe actions, stop when evidence is missing, and explain what it did afterward. That distinction is easy to miss because most demos test the wrong thing. A demo asks, “Can th
Most agent failures are not model failures. They are loop failures. The model may be smart enough. The tools may be correctly defined. The prompt may even be reasonable. But the agent still repeats itself, misreads a tool result, burns through retries, or confidently turns a transient API timeout i
I ran a two-minute test on three different AI assistants last week that I can't stop thinking about. You can reproduce it yourself in about the same amount of time. Take a real decision you're actually weighing — not a toy example, but something where you're genuinely undecided. Open three separa
Almost every RAG evaluation metric on offer needs a language model to produce it. Faithfulness, answer relevance, context precision: a model reads the answer and scores it. Those are good metrics. They measure things that are hard to measure otherwise, and for a research sweep or a quarterly qualit
hey all, I’m looking for advice how to build build better charts. I have a site that pipes API data into recharts that become part of a report (example). Broadly, it's working but I still have instances in which I get text that meshes together (See image). There are too many potential charts for me
I build on a connection that drops for hours at a time. That exposed an assumption nearly every agent memory library makes without stating it. In most of them, remember() calls an embedding API, so the write fails when the connection does. Reversing the order It is an ordering decision,
Disclosure: I work on WAME API, a Meta Business Partner. The payload format below is what our platform emits, but the structural idea — normalizing three channels into one envelope — applies to any integration you build yourself. If you have ever wired WhatsApp into a product, you know the shape o
Muse launched September 8: a Meta agent that emails, books, fills forms and pays. US only, 18+ Free up to 100M tokens a week, card required, then 20 USD Power or 100 USD Maximum Each user gets a Linux VM with its own browser, and a separate Sentinel agent approves every network call Reuters saw inte
Why passkeys still stall with normal users Passkey teams usually know the security case already. The harder part is adoption: how do you get someone non-technical to actually say yes when the current login still works? That question matters because many passkey rollouts underperform for reasons t
Imagine a customer sends this message: “I was charged twice, but one of the charges looks different from the other. Can you figure out what's happening?” There isn't an obvious workflow here. The system needs to understand what the customer means, look at their account, identify the transactions, ch
Repo: https://github.com/wellparth/almost-cli Most coding assistants rent you a model and keep your code in the cloud. almost-cli takes a different approach: bring your own API keys, run a team of specialized agents locally, and keep everything under your control. The idea almost-cli is
"Does your AI code reviewer read CLAUDE.md?" is the wrong question to ask. Almost any of them will say yes, and honestly, most will catch something. What actually matters is whether you can tell the difference between a tool that read your rule and a tool that just happens to agree with it. So I bu
I'm Marco. Twenty-five years as a TV cameraman, most of it on production sets, some of it on cruise ships, then Spain, then the UK, then Dublin, where I live now. Photography came later — my own thing, on my own time, separate from the job. I'm not a formally trained engineer. Everything you're abou
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