AI 资讯

আমার colleague এখন প্ল্যান দেখতে পারছে। Scan types বুঝতে পারছে। Join types বুঝতে পারছে। Estimate আর actual এর gap দেখতে পারছে। BUFFERS ও দেখছে। কিন্তু সে প্রশ্ন করল। এসব দেখে কি করব? Step by step কোন পথে যাব? আমি বললাম। পাঁচটা step আছে। অর্ডার অনুযায়ী। পর্ব ২ এ আমি বলেছিলাম scan types, join types, estimate আর actual এর gap। BUFFERS কি। এবার আসি সমাধান এ। Diagnostic Workflow আপনার কাছে একটা slow query এসেছে। কিভাবে debug করবেন? এই পাঁচটা প্রশ্ন করুন অর্ডার অনুযায়ী। ৯০% slow query প্রথম বা দ্বিতীয় ধাপেই solve হয়ে যায়। ১. Deepest Seq Scan দেখুন Table বড় কি না? Filter selective কি না? Missing index থাকলে add করুন। আজই শুরু করুন যখন একটা Seq Scan দেখবেন big table এ, প্রথমে WHERE clause টা check করুন। Selective কি না? ৫% এর কম row return হওয়ার কথা? যদি তাই হয়, index missing। CREATE INDEX idx_name ON table(column) run করুন। ২. Join types দেখুন কোনো Nested Loop আছে কিন্তু দুই পাশেই বড় table? Hash Join force করুন বা ডান পাশে index add করুন। আজই শুরু করুন Nested Loop দেখলে ডান পাশের table এ index check করুন। যদি না থাকে, create করুন। Index থাকা সত্ত্বেও planner Nested Loop use করছে? SET enable_nestloop = off temporarily disable করে দেখুন। Hash Join আসবে কি না। ৩. Row estimates দেখুন Estimate vs actual ১০x এর বেশি difference? ANALYZE table দিন বা predicate rewrite করুন। আজই শুরু করুন rows=1 estimate কিন্তু rows=100000 actual দেখলে ANALYZE tablename run করুন। Statistics refresh হবে। তারপর plan আবার দেখুন। যদি তাও না আসে, WHERE clause rewrite করুন। Function call থাকলে remove করুন। Type mismatch থাকলে fix করুন। ৪. BUFFERS add করুন কোনো node এ অনেক disk reads? Caching investigate করুন। আজই শুরু করুন EXPLAIN (ANALYZE, BUFFERS) run করে দেখুন shared read high কোথায়। সেই node টাই bottleneck। Index add করলে reads কমবে। Pre-warm cache করতে পারেন। Data pre-load করতে পারেন। ৫. Sorts আর hashes দেখুন কোনো spill-to-disk আছে? work_mem raise করুন বা sort eliminate করুন। আজই শুরু করুন Plan এ external merge Disk: 421MB দেখলে spill-to-disk হয়েছে। SET work_mem = '256MB' temporarily rais

I have written some version of "never hardcode secrets, store them in Key Vault instead" in at least five of my last nine posts on this blog. I never actually stopped to explain what that means in practice. This post fixes that, using the real secrets this very blog depends on: a database connection string, an admin panel password, and a set of GitHub deployment credentials. What Azure Key Vault Actually Is Azure Key Vault is a managed service for storing secrets, encryption keys, and certificates securely in the cloud. Instead of a password sitting in a configuration file or a public GitHub repository where anyone with read access can see it, the password lives in Key Vault - encrypted, access-controlled, and logged every single time it is read. Picture your code as a house with see-through walls - anyone looking at the repository can see everything inside, including any password left lying on the kitchen table. Key Vault is a bank vault a few streets away. Your code does not hold the password directly; it holds a key card that lets it walk over and request the password at the exact moment it is needed. Lose the key card, and you still cannot get into the vault without proper identity verification on top of it. Three Things Key Vault Stores Secrets are plain string values - passwords, connection strings, API keys, tokens. Keys are cryptographic keys used for encrypting and decrypting data, or for signing and verifying it. Certificates are X.509 certificates used for TLS or client authentication between services. Most applications, including this one, primarily use the Secrets feature. An Honest Admission About TechStackBlog's Own Setup This blog does not actually use Key Vault directly today. The database password lives in Azure App Service Configuration. The admin panel password and deployment credentials live in GitHub Secrets. For a single-application personal project, this is a perfectly reasonable and secure setup. Key Vault earns its place once you have multi

Predict anything with AI Discussion | Link

Cross-posted from the LLMKube blog . A local 27B coding model, running on hardware in my house, is a coin flip. Some runs it nails the fix in twenty minutes. Some runs it edits the wrong file, writes a test that passes no matter what the code does, and tells you it's done. The bet behind LLMKube's Foreman was never that I would find a local model good enough to trust. It was that I could build a harness I trust more than any single model's output. This weekend tested that bet harder than any benchmark could, because the harness spent the weekend building its own guardrails. Here is the short version of what happened across 0.8.12 and 0.8.13. My local coder built three new gates for itself. One of them shipped with the exact flaw it was written to catch, and the review caught it. Three new contributors sent four clean pull requests while the machines worked. The same model ran on an AMD box and an Apple Silicon Mac, and the Mac quietly won a round nobody expected. And not one byte of any of it touched a cloud API. The thesis, stated plainly Trust the harness, not the model. A coding agent on a local model produces output of wildly variable quality, and no amount of prompt tuning makes a 27B as reliable as a frontier model. So Foreman does not ask the model to be reliable. It wraps the model in a pipeline that is : the coder works in a cloned workspace, a fast in-workspace gate runs gofmt, vet, build, lint, and the unit tests for the packages it touched; a reviewer reads the diff against the issue; and a clean-room Kubernetes Job re-runs the full suite before anything is allowed to call itself a GO. Around all of that sit deterministic rails: scope checks, edit-free-streak detection, repo-map context. The model is a stochastic component inside a system whose job is to make the system's verdict trustworthy even when the component is not. The interesting question is never "is the model good." It is "does the harness catch the model when it is bad." This weekend gave me

Treating docs as a product When documentation lives as an afterthought, it shows. Pages drift out of date, examples break quietly, and release notes scatter across a dozen places no one can find. The fix is not a weekend cleanup. It is a decision to treat docs the way you treat any product people depend on: someone owns it, it has standards, and it gets maintained on purpose. That is the decision I made when the docs came to the Developer Relations team at the end of 2025. Not "let's tidy this up," but "this is ours now, and we are accountable for whether a developer can actually build from it." The work, in the repository The honest record of what a team does to a codebase lives in its git history, so that is where the story starts. Comparing the six months before the handoff to the six months since: Before vs. Under DevRel: Commits: 476 → 1,900+ Merged pull requests: 145 → 447 Unique contributors: 21 → 64 A repository that averaged fewer than 500 commits over half a year is now past 1,900 in the same span. The contributor count tripled, because we treated the docs as something the whole community could improve, not a walled garden. This is what a team that decided to do the work looks like when you measure it. Our proudest metric is what was cut In six months, we added roughly 339,000 lines and removed roughly 281,000. That near balance is the point. A neglected docs site accumulates: dead pages, stale tutorials, examples that no longer compile, three slightly different explanations of the same concept. Adding more on top of that does not help anyone. So we cut nearly as much as we wrote. We rebuilt the Hello World walkthrough from 1,300 lines down to about 300 without losing a thing. We consolidated scattered release notes into a single clean reference. A docs site is judged by what a developer can find and trust, not by how much sits on the shelf. A library you can learn from At the start of the year, the examples library had effectively one usable entry. Today,

https://archive.is/6BUJw

Alina Krasavina explains how Delivery Hero successfully deprecated Google Analytics and migrated to an internal user tracking platform. She discusses how a simplistic, highly scalable architecture allowed them to handle 10 times more load while capturing 97% of tracking data. By Alina Krasavina

Prime Day has brought a number of Apple deals, but one of the most useful if you’re planning to travel over the July 4th weekend or later this summer is on the new AirTags. The four-pack has dropped to a new low of $90 ($9 off) at Amazon and Best Buy, bringing the price of […]

Add WebMCP-native AI chat to any Frontend Discussion | Link

Before I released my own AI chatbot plugin, I ran it through a security review. It came back with 35...

Gareth Martin drew on diverse inspirations for Signet City, from horror manga to a musical from Sting.

Instagram's TV app is expanding to Samsung TVs in the US, half a year after it launched for Amazon Fire TV devices.

Hi HN, I'm Ahmad from the Intuned ( https://intunedhq.com ) team. Today, we're releasing and open-sourcing Selector Forge ( https://selectorforge.ai/ ), a browser extension that generates reliable CSS/XPath selectors using AI. You can use it to create a selector for a single element or for an array of elements. The selectors it creates are meant to be "semantic" and more resilient to page changes than what Chrome DevTool’s “Copy Selector” (and other similar extensions) give you. Those tend to ha

The rails AI agents use to find and pay you Discussion | Link

Today, I’m talking with Jack Conte, the CEO of Patreon. Jack last joined me on the show almost exactly five years ago, in the summer of 2021, and a lot has changed on the internet and in the creator landscape since then, so I was very excited to talk to him again, especially since his […]