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Originally published on DevToolHub . Helm 4 shipped in November 2025. Eight months later, most teams are still running Helm 3 in production CI/CD because it works. But Helm 3's final feature release lands September 9, 2026, and security patches stop completely on February 10, 2027. This helm 4 migration is simpler than it looks. Your charts don't need rewriting — Helm 3 Chart API v2 charts are fully compatible with Helm 4. But the automation around Helm has four real breaking points that fail silently if you don't know where to look. [IMAGE: articles/images/2026-07-05-helm-4-migration-guide-featured.png | alt: "helm 4 migration flow from Helm 3 to Helm 4 upgrade path"] Why This Helm 4 Migration Matters Now The EOL timeline has three stages, and they matter differently based on your situation: September 9, 2026 — Final Helm 3 feature release (limited to Kubernetes client library updates only after this date) February 10, 2027 — All security patches stop If your organization runs regulated workloads with requirements around supported software, February 2027 is your hard deadline. But waiting until then means doing this migration under pressure, after 14 months of Helm 4 fixes shipped without you tracking them. The better path: upgrade now, before September, so you're on supported software when new Kubernetes releases land and need updated client libraries. What Actually Broke: The Four Real Changes 1. Post-renderers require plugin registration In Helm 3, you could pass any executable directly to --post-renderer : helm install myapp ./chart --post-renderer ./scripts/mutate.sh Helm 4 drops this. Post-renderers must now be registered as named Helm plugins and referenced by plugin name: helm install myapp ./chart --post-renderer my-post-renderer If your pipeline calls --post-renderer ./path/to/script.sh , it fails on Helm 4. The error message doesn't say "plugin required," so this is easy to miss in a quick smoke test. To wrap an existing script as a plugin, create a plug

I'm launching SmartTaxCalc.in on Product Hunt on Tuesday, 14 July 2026 . It's a 38-tool Blazor WebAssembly tax + finance calculator I've written about here before ( the SEO/schema saga , and dropping mobile LCP from 6-8s to under 2s ). The Product Hunt launch algorithm heavily rewards products that arrive with a real coming-soon follower base — day-of upvotes correlate strongly with pre-launch "Notify me" clicks. My PH page started with 1 follower . I had 9 days to get to 50+. The obvious answer: post on LinkedIn, ask friends, DM your network. All of that has ceilings (you can only ask a favor once). The non-obvious answer that has no ceiling: convert your own organic search traffic into PH followers automatically. This is the ~100 lines of Blazor code that does that, plus the design decisions I made along the way. It's also self-cleaning — after the launch date, the banner disappears with no manual work required. Steal the pattern for your own launch. The problem SmartTaxCalc gets modest but real organic traffic — mostly from Google Search Console impressions on tax-season queries. That traffic is the warmest possible audience for a PH launch (they already found the site, they're in the target demo). But how do you route them to a PH page without: Disrupting the tax content (they came for a tax calculator, not a marketing pitch) Cannibalizing the existing tax-season banner (which drives users to /tax-calendar/ — a real retention lever) Leaving code debt after 14 July (a dead PH banner still on the site in September) Losing the dismiss preference across page navigations (SPA reality — no page refresh) Those constraints ruled out a modal, a full-width interrupt, and a "hardcoded remove after launch" approach. The design Slim horizontal bar at the top of every page. Sits ABOVE the existing tax-season banner. PH-brand orange, different from the tax-season banner's yellow/red so both are visually distinguishable when stacked. Dismissible per-user via localStorage . Auto

Sometimes races finish behind a safety car, but it's not always satisfying.

The previous code-graph series was about reshaping a static analysis graph so AI could query it. The same kind of reshaping is needed on the observability side. This post walks through four axes — application / infrastructure / CI / LLM — and the deliberately different shapes each one ends up in. The design judgments worth calling out: computing Gemini cost client-side instead of from billing API, sending Claude Code OTel straight to BigQuery instead of Loki, and shipping CI logs via post-hoc pull instead of webhook push.

Abstract Cada vez más equipos quieren consultar datos sin escribir SQL a mano. El problema es que un sistema Text-to-SQL no solo debe “traducir preguntas”, sino también entender el esquema, restringir permisos, validar consultas y explicar por qué una consulta es rápida o lenta. Ese enfoque coincide con la ruta propuesta por el tutorial oficial de LangChain para agentes SQL: listar tablas, inspeccionar esquemas, generar la consulta, revisarla, ejecutarla y corregir errores hasta obtener una respuesta; y el propio tutorial advierte que ejecutar SQL generado por modelos tiene riesgos y exige permisos mínimos. En paralelo, la documentación oficial de Python recomienda usar placeholders en sqlite3 para enlazar parámetros y evitar inyección SQL, mientras que la documentación de SQLite explica que EXPLAIN QUERY PLAN permite inspeccionar si una consulta hace SCAN, SEARCH y si usa índices. manueldongo23 / sql_ai_sales_assistant_demo SQL AI Sales Assistant A safe Text-to-SQL demo that converts natural language business questions into SQL queries, executes them on a local SQLite retail database, and shows the generated SQL plus EXPLAIN QUERY PLAN . This project was created as evidence for an article about SQL AI Database Solutions . Topic Talk to your database with AI: build a safe SQL query extractor with Streamlit and SQLite. Features Natural language prompts such as sales by month , top customers , sales in Lima . Rule-assisted NL→SQL generation, designed to be transparent and auditable. SQLite demo database with customers, products, orders and order items. Read-only SQL validator that blocks destructive commands. Parameterized queries for user-provided filters. Streamlit interface. CLI demo for quick testing. Query plan inspection with EXPLAIN QUERY PLAN . Architecture User question ↓ NL → SQL interpreter ↓ Read-only SQL validator ↓ SQLite execution ↓ Results + EXPLAIN QUERY … View on GitHub Cuerpo del artículo La promesa de SQL + IA suena sencilla: le haces una pregunta

Building an AI agent sounds complicated. It's not. By the end of this guide, you'll have a working agent that can search the web, remember conversations, and handle multi-step tasks. No frameworks, just TypeScript and an LLM API. What We're Building A research assistant agent that: Takes questions from users Uses tools (web search) when needed Remembers conversation history Handles errors without crashing Runs in about 150 lines of TypeScript This won't be production-ready, but it'll work and you'll understand every line. Prerequisites You need: Node.js 18 or higher Basic TypeScript knowledge An Anthropic API key ( get one free ) That's it. No prior AI experience needed. Setup (5 minutes) # Create project mkdir research-agent cd research-agent npm init -y # Install dependencies npm install @anthropic-ai/sdk dotenv # Install dev dependencies npm install -D typescript @types/node tsx # Initialize TypeScript npx tsc --init Create .env : ANTHROPIC_API_KEY = your-key-here Step 1: Define Your Types Create src/types.ts : export interface Message { role : ' user ' | ' assistant ' ; content : string ; } export interface Tool { name : string ; description : string ; input_schema : { type : ' object ' ; properties : Record < string , any > ; required ?: string []; }; execute : ( input : any ) => Promise < string > ; } Why these types matter: Strong typing prevents bugs. If you change how a tool works, TypeScript tells you everywhere that breaks. Step 2: Create a Simple Tool Create src/tools/search.ts : import { Tool } from ' ../types ' ; export const searchTool : Tool = { name : ' search_web ' , description : ' Search the internet for current information. Use this when you need facts, recent events, or data you do not know. ' , input_schema : { type : ' object ' , properties : { query : { type : ' string ' , description : ' The search query ' , }, }, required : [ ' query ' ], }, execute : async ( input : { query : string }) => { console . log ( `[Tool] Searching for: ${ input

AI automation agent to get stuff done across apps or browser Discussion | Link

Microsoft cut around 4,800 roles, or 2.1% of its global workforce, on Monday — the latest in a series of layoffs that’s stoking fears of AI replacing jobs. The layoffs will hit Xbox and commercial sales the hardest.

USB-C promised simplicity, but has turned into a mystery box of different specs.

In the AI era, platforms have no choice but to fight fire with fire to cull spam.