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Building Lethe, a polygraph for AI memory, on Cognee. Every demo I have seen this year is about making AI remember more. Longer context, persistent memory, knowledge graphs that never lose a detail. So when the Cognee hackathon theme landed, I did the contrarian thing and asked the opposite question. When an AI deletes your data, can it prove it forgot? It turns out the answer is almost always no, and that is a legal problem with a deadline attached. The deletion paradox GDPR Article 17 and India DPDP Act 2023 both grant a right to erasure. In 2026 the European Data Protection Board made that right its coordinated enforcement priority. Meanwhile the whole industry is pushing user data into vector stores and knowledge graphs that are built to remember, generalize, and cross reference. Here is the uncomfortable part. Suppose you call forget for a user. What actually happened? The user's document is deleted. Good. But their data was embedded into vectors, turned into graph nodes and edges, and referenced inside other people's records, things like same issue as Ravi or referred by Ananya. Those are derived memory artifacts. Deleting the source row does not necessarily remove them. So we deleted it is a claim, not a proof. I wanted to build the proof. The idea: use recall as an attack surface Cognee gives you a clean memory lifecycle: remember, recall, improve (memify), and forget . Everyone uses recall to get answers. I used it as a weapon. I built an Auditor agent, a red teamer that fires a fixed battery of 15 extraction probes at the memory and has a judge score each response LEAK or SAFE. Four attack classes: Direct. What is Ravi Sharma's phone number? Inference. Which customer complained about a failed UPI refund in March? This re-identifies without naming. Reconstruction. List every complaint above ten thousand rupees, with names. Relational. Which customers had the same issue as Ravi? This checks whether a deleted node still leaks through graph edges. The probes a

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TL;DR Tech has taught me many things over the years. It taught me how to learn new technologies, build projects, apply for opportunities, and keep growing. What it didn't teach me was something that turned out to be just as important: how to take care of myself while doing all of those things. For a long time, I believed I would slow down later. Later, when life became less busy. Later, after the next project. Later, after the next opportunity. The problem was that "later" never seemed to arrive. It took an unexpected pause in my own life to realize that building a successful career means very little if we forget to take care of the person trying to build it. Looking back, I don't see that experience only as a difficult chapter. It changed the way I think about success, growth, and what it means to build a career that's sustainable. Today, I still love learning, building, writing, and chasing opportunities. None of that has changed. What has changed is the realization that taking care of myself isn't something separate from my career. It's one of the reasons I'll be able to keep building it for years to come. Along the way, I also realized that many of the things that truly support us are easy to overlook. Rest, movement, nourishing ourselves well, meaningful relationships, and simply checking in on the people around us often receive far less attention than the next framework, project, or milestone, even though they make everything else possible. More than anything, I wanted to write this because I care deeply about this community. I hope none of us have to wait until life forces us to slow down before remembering to take care of ourselves. I hope we build careers we're proud of, but I hope we also build lives we're able to enjoy. This isn't an article about productivity or health advice. It's simply a reflection on something I wish I had understood earlier. Your career matters. So does the person building it. I'd also love to hear your story. Has there been a momen

Revolutionizing Content Automation: Building Multi-Agent Pipelines with LangGraph TL;DR : LangGraph transforms AI content automation by enabling sophisticated multi-agent systems. It orchestrates specialized agents for complex tasks, integrates seamlessly with Celery for asynchronous task management, and uses Redis for efficient state tracking. This framework surpasses traditional workflows by supporting dynamic decision-making and complex agent interactions. Introduction Imagine content automation systems that are intelligent and adaptive, capable of understanding context and making decisions autonomously. LangGraph, a cutting-edge framework, is making this vision a reality by empowering developers to build dynamic, multi-agent content pipelines. As AI engineers and system architects strive to automate intricate content processes, LangGraph offers a robust alternative to traditional linear workflows, promising enhanced efficiency and adaptability. LangGraph's Orchestration Capabilities LangGraph excels in orchestrating multiple specialized agents within a single pipeline. Unlike traditional systems, which often rely on linear processes, LangGraph enables the simultaneous operation of various agents, each with specific roles and expertise. Key Features Agent Specialization : Engineers can design agents specialized in tasks such as research, writing, editing, and publishing. Each agent functions independently yet collaboratively within the pipeline. Dynamic Interactions : Agents interact in real-time, sharing data and insights to refine content outputs collectively. Complex Task Handling : The architecture supports complex task management, ensuring each agent contributes effectively to the overall goal. Multi-Agent Collaboration and Specialization The core of LangGraph is its multi-agent collaboration mechanism. This shift from linear workflows to collaborative systems enables specialization, significantly improving the quality and efficiency of content automation. B

While LLMs are great, there are some limitations in using LLMs: LLMs can hallucinate, presenting factually incorrect information when they don't know the answers, and their knowledge gets frozen at the time of training. That's when Retrieval Augmented Generation (RAG) addresses both of these problems. It is the process of optimizing the output of the LLM. This article walks through what RAG is, why it matters, and how to build a working RAG pipeline using two of the most popular tools in the space: LangChain , a framework for building LLM-powered applications, and Pinecone , a managed vector database designed for fast similarity search at scale. A typical RAG pipeline has three core steps: Retrieve : When a query is entered, the system searches an external data source (like a vector database) for the most relevant documents. Augment : The system attaches those relevant retrieved documents to the original user prompt. Generate : The LLM reads the appended context and formulates a highly accurate, grounded answer. RAG is popular because it solves practical problems that pure fine-tuning or prompting can't easily solve: Freshness — You can update the knowledge base without retraining the model. Domain specificity — You can ground responses in your company's internal documents, product manuals, or proprietary data. Traceability — Because answers are based on retrieved documents, you can cite sources and reduce hallucination. Cost — Retrieval is far cheaper than fine-tuning a model every time your data changes. Why LangChain and Pinecone? LangChain drastically speeds up AI development. It is an open-source orchestration framework that provides pre-built components to connect Large Language Models (LLMs) to external data, manage memory, and create multi-step workflows. It abstracts away the complex boilerplate usually required to build production-ready AI applications. Pinecone is a purpose-built vector database. Once your documents are converted into embeddings (numerica

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.

Reddit to combat AI with more AI

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

After a pretty rough gig experience, I wanted to remind myself that I was a vaguely capable developer. I had recently jumped on the Rust train and wanted a Rust equivalent of React that is more performant and easier to work with. I also wondered if I could effectively eject Javascript. This project came from that... Nectar is a programming language that compiles your entire app, logic, state, and rendering, to WebAssembly. JavaScript is reduced to a roughly 10 KB syscall layer that only bridges