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Part 1, Your AI Agent Doesn't Need to See Every Tool, argued for hiding low-level tools behind domain functions like investigate_delivery_issue() that hide the API calls underneath. Part 2, Not Every Step Needs an LLM, went inside those functions and separated work that belongs in code
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I still remember the first production ETL job I inherited. It ran at 2 AM, and if it failed, nobody knew until someone in finance emailed asking why yesterday's revenue numbers looked wrong. We'd scramble, find a schema change nobody flagged, patch it, and rerun. Rinse
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If you’ve ever inherited a Python backend built with FastAPI, Django, or Flask that grew past 50,000 lines of code, you know the dread. A request to change a database field cascades into modifying serializers, view functions, background workers, and unit tests. Everything is coupled to everything else.
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A few months ago I watched a fraud model flag a transaction correctly. The only problem was that it flagged it four minutes after the money had already left the account. The model was fine. The math was fine. The data pipeline was the thing that failed, quietly, the way
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Building a Fully Decentralized & Encrypted AI Diagnosis Fleet Using Python and IPFS. The biggest flaw of the current Artificial Intelligence boom is centralization. Every time a user interacts with a medical AI chatbot or uploads a diagnostic report to a cloud server, they surrender their ultimate right: data privacy.
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Two exposure-management tools can examine the same environment and return very different numbers. That does not necessarily mean that either is wrong. 15,000 exposures, fewer than one per cent of them CVEs In May 2024, XM Cyber and the Cyentia Institute estimated that a typical organisation had about 15,
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We have entered a new era. I sometimes think of Jean-Jacques Annaud’s Quest for Fire. In the film, fire is not simply a tool. Mastering it fundamentally changes the human condition. Artificial intelligence is creating a shift of the same magnitude, probably an even deeper one. Fire increased our
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Three .NET services on an AKS cluster each pinned their CPU limit while doing no useful work. They targeted a TLS-only RabbitMQ listener with a plaintext client. Their configuration omitted the AMQP TLS flag, and the older framework package did not wire that flag into the connection factory at all.
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A deployment pipeline stopped at its migration step. A check of PostgreSQL activity showed no active queries and no sessions idle in transaction. The latest container log showed the immediate cause. drizzle-kit push 0.30.4 had opened an interactive menu inside a one-off migration job, where no operator could
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Say you run support for a bank. A customer opens the chat and asks: What are the terms of the family mortgage? You have to answer. Instead of reaching for a framework or the "ideal" RAG diagram, let's just build it. Start with the simplest thing,
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Key Takeaways * Deploying an ML model to a fleet of physically separate industrial plants is not the same problem as deploying to a fleet of cloud regions: the deployable unit has to be the model plus its site-specific configuration, versioned together, not the model alone. * A validation gate which only
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Every migration deck eventually gets to the reassuring line: zero data loss. It sounds definitive, which is probably why teams like saying it. The problem is that saying nothing was lost and proving nothing was lost are two completely different engineering tasks. I learned this the hard way while leading
