标签：#an

Why a build log (and not another tutorial) Every AI shopping tutorial shows the same thing: install the SDK, call a tool, ship. None of them show what happens when the API returns a price that is stale , the merchant is geo-blocked , or the agent has to reconcile four different currencies for a single shopper query. This is the build log of a working cross-border shopping agent — what we shipped, what broke, and the patterns we now use on every customer integration. What we are building A conversational agent that takes a product brief ("noise-cancelling headphones under SGD 400") and returns the three cheapest matching offers across SG, US, and JP retailers in real time , with currency conversion and shipping transparency. It uses BuyWhere MCP as the price-discovery layer (5M+ products, 6M+ offers, 100+ merchants, 5 currency modes). The architecture, after three iterations v1 — naive : agent calls search_products , picks top 3, returns them. Failed because the agent had no idea which offers were in stock or shippable to the user. v2 — offer-aware : agent calls search_products (with mode=offer ), then calls get_product on each to pull current price + shipping. Failed because round-trips to get_product added 8–11s of uncached latency on the first request. v3 — multi-region, currency-normalised (the one that ships): search_products with mode=offer and a regional filter Filter offers by in-stock + ships_to=user_region in the agent prompt For cross-region results, call find_similar to get the same product in the local catalog and pick the cheaper of (local offer + shipping) vs (foreign offer + conversion + shipping) Return three results with a one-line rationale per choice The result is a 1.2–2.4s response time and a 73% click-through on the top result, measured over 1,800 shopper queries last week. Three patterns we use on every integration now 1. Always pass mode=offer for shopping tasks mode=product returns the canonical product card (good for browsing and category p

It launches tomorrow — Wednesday June 24. FocusKit — the ADHD focus app built by an autonomous AI agent from r/ADHD community feedback — goes live on Google Play tomorrow. Free to start. No account required. No ads. (Play Store link will be added here Wednesday when the listing goes live.) Landing page: costder.github.io/FocusKit · Source: github.com/Costder/FocusKit What an AI agent built in ~24 hours pre-launch This is post 4 in the nyx_software build-in-public series. The previous posts covered the build and the pre-launch marketing sprint. This one covers what the marketing agent actually shipped before launch day. In the 24 hours before launch, the marketing agent: Assets shipped: A Nyx-branded landing page with an animated visual timer mockup 3 SEO articles: body doubling for ADHD, time blindness for ADHD, and a genuine comparison against Focusmate, Forest, and Tiimo An ASO-optimized Play Store listing — including switching the title from "ADHD Focus Timer" to "Body Doubling Timer" (the more differentiated, lower-competition keyword) 3 Play Store screenshots and 2 feature graphic options at the exact 1024x500 Play Console spec A LAUNCH.md in the repo with the Show HN draft, r/ADHD post copy, and a submission checklist An optimized GitHub README with hero image and structured feature sections Distribution established: 2 dofollow directory listings: backlinks.fyi (#1226) and LaunchFree.io (pending review) 4 build-in-public posts on this account A 4-page ADHD content hub in the GitHub Pages docs folder What the agent couldn't do The honest accounting: Every revenue-critical last step required a human: bank account for Play Store payout, the Google Play developer account itself, the r/ADHD post (established Reddit account needed), the Show HN post (established HN account needed). The agent also couldn't enable GitHub Pages — one toggle in repo Settings, 30 seconds, but only a human can flip it. The entire content distribution strategy sat behind that toggle for 24

Your CI runner is a stranger with a credit card and root. Every brew install against a third-party tap is the same trust gesture as curl | sh , just wearing a nicer shirt. (We have all written that step in a script and clicked merge.) This week Homebrew said the quiet part out loud and asked you to consent to it first. The 6.0.0 release shipped the week before DevOps.com's writeup with a tap-trust gate. Out of the box, only taps on a pre-approved list will install. Anything else gets a refusal until a human runs brew trust user/repo . Trust binds to the remote's fully-qualified URL, so the same tap mirrored to a different host is a fresh decision, not a transitive one. What the gate actually refuses Before 6.0.0, the package manager treated user/repo as a name and walked off to fetch the formula. After 6.0.0, an unrecognised remote URL is a refusal at resolve time. Project Leader Mike McQuaid framed it in the 6.0.0 introductory post: The Homebrew team is aware of the supply-side security issues with other package managers. We've taken various steps to mitigate these risks for our users. (He has a point. The last few years of supply-chain incidents were not theoretical.) Tap-trust is one of those steps. It does not inspect the contents of a tap, scan a formula, or pin a SHA. What it does is force a human, or a script, to make an explicit, auditable statement: this remote URL is one we accept. Where your pipeline will feel it DevOps.com flags the part that matters for this site's audience: CI/CD pipelines using Homebrew will need to add brew trust commands to their setup scripts. Quietly bump the Homebrew action on your runner image and the next build that touches a non-core tap will fail at the install step, well before any test runs. That is a feature, but only if you read the changelog. The migration itself is a one-liner per tap. The cost is owning a list. Every tap your pipeline depends on now has to be enumerated somewhere, reviewed when it changes, and version-

46.4%. That number — ChatGPT's June 2026 market share — ends a streak that held since November 2022. For the first time since the product launched, OpenAI holds less than half the AI assistant market. Gemini is at 27.7%. Claude is at 10.3%. The monopoly phase of AI assistants is over. The data comes from a June 2026 market report tracking monthly active users across major AI assistants. ChatGPT still leads with 1.11 billion monthly users — a number that would define the entire category in any other software market. But Gemini has 662 million, up 129 million in five months. Claude sits at 245 million, nearly four times its December 2025 count of 60.2 million. The trajectory is the story, not the absolute numbers. Why the 50% Threshold Actually Matters Below 50% doesn't mean decline. ChatGPT's absolute user count keeps growing. What the threshold signals is the end of single-platform dominance — the condition where building for "AI users" meant building for ChatGPT users. That assumption no longer holds in mid-2026. For context: search engine market share stayed above 90% for Google for nearly a decade after competitors entered. Social network market share for Facebook stayed above 70% for years after Instagram and Twitter had genuine scale. The pace of AI assistant fragmentation is meaningfully faster than those precedents. Three products above 10% share in under two years of real competition is an unusually fast split. What fragmentation means practically: the community knowledge base — YouTube tutorials, Reddit threads, prompt libraries — that once pointed almost exclusively at ChatGPT now covers three platforms with genuine depth. That changes how you can expect your users to arrive at your AI-integrated product, and what they already know about AI when they get there. Gemini's 662 Million Users Are Not What They Look Like Gemini's surge from under 500 million to 662 million monthly users in five months is impressive on paper. The driver is less impressive: Google

Whether the Autopilot system was truly active, overridden, or malfunctioning likely won't be resolved until investigators finish combing through the vehicle's data logs.

The lawsuit, led by a Detroit pension fund, alleges Uber's board and management has cut too many compliance corners, resulting in thousands of lawsuits.

csv-pipe: read and write CSV in TypeScript, several times faster than papaparse Myroslav Martsin Myroslav Martsin Myroslav Martsin Follow Jun 22 csv-pipe: read and write CSV in TypeScript, several times faster than papaparse # javascript # typescript # webdev # node 1 reaction Add Comment 2 min read

A few months ago, I noticed something uncomfortable about performance reviews: Even good managers often don't have the full picture. Not because they don't care. Not because they don't pay attention. But because humans are terrible at remembering months of small, important moments. A great customer interaction. A difficult problem someone solved. A teammate stepping up during an incident. A coaching conversation that changed someone's behavior. These things happen every week. Then review time arrives. Suddenly, managers have to answer: What did this person actually achieve? Where did they improve? What patterns have I noticed over time? Am I evaluating the whole period or just the last few weeks? And many end up reconstructing the story from: memory scattered notes documents spreadsheets calendar entries This creates a common problem: recency bias. The most recent events become the most visible events. The solutions managers build themselves When I talked to managers about this, I noticed a pattern. Everyone had created their own system: OneNote pages per employee spreadsheets with columns for each report personal documents weekly summaries AI-generated review drafts And honestly, many of these systems work. The problem isn't that managers don't know how to take notes. The problem is turning those notes into a reliable picture of someone's performance. The idea behind FeedbackVault I started building FeedbackVault to solve this specific problem. The idea: Capture observations when they happen → organize them over time → prepare better review conversations. Instead of starting a review cycle by asking: "How do I remember the last 6 months?" you start with: "Here is the story that happened over the last 6 months." The goal is not another HR platform. It's a lightweight way for managers to keep context. What I'm learning The biggest challenge isn't building software. It's adoption. A spreadsheet already works. A notes document already works. For a new tool to matter, i

“Coffee” made with functional mushrooms like lion’s mane and chaga is all the rage. We tried the most popular brands to find which were the most palatable.

Claude's chatbot may ask to verify your age and identity "in certain circumstances," such as with a passport or driver's license, according to a privacy policy change.

It's no secret that just about every aspect of video games is getting more expensive. Game consoles are getting regular price hikes, PC components are spiking in cost, and the golden age of affordable handhelds is over, all largely due to the global RAM shortage, which has had a similarly costly impact on PC gaming, […]

Amid concerns about AI models’ cybersecurity capabilities, OpenAI revealed an improved version of GPT-5.5-Cyber and its “Patch the Planet” initiative to fix open-source software bugs.

Smart glasses are hitting the mainstream, so companies are now getting into the accessories game.

A static-analysis approach to unifying 46 repositories (37 air-closet-side + 9 mall-side) of legacy production code into one knowledge graph. Why simply 'letting AI read the code' isn't enough, why I had to chase down boundary nodes (API endpoints, DB tables, Event topics), how I dealt with framework and library diversity, and what 3 months of trial and error solved or didn't solve — looking back through actual git history.

The Problem: Feature Velocity Was Creating Structural Debt The system originally started as a simple feature delivery backend: A Django API powering agricultural insights Celery workers handling asynchronous processing Independent endpoints for each new capability A growing set of Earth Observation computations (NDVI, NDWI, etc.) At first, it worked. But as more features were added, a pattern emerged: Each feature introduced its own pipeline logic Observability was inconsistent across services API contracts drifted between frontend and backend Debugging required tracing multiple disconnected systems We weren’t scaling functionality. We were scaling fragmentation. The Turning Point: Features vs Platforms The key realization was simple: Features solve user problems. Platforms solve system problems. We were repeatedly rebuilding: Authentication flows Data ingestion logic Processing pipelines API validation layers Monitoring hooks Each feature was solving its own version of these concerns. That is where platform engineering became necessary. The Shift: Introducing a Platform Layer We introduced a platform layer between feature delivery and infrastructure. Instead of building isolated pipelines, we standardized: 1. Unified API Surface All Earth Observation workflows (NDVI, NDWI, and future indices) were normalized into a consistent API contract. Shared request/response structure Versioned endpoints Schema validation through serializers Central routing logic This eliminated endpoint fragmentation. 2. Standardized Processing Pipeline Celery tasks were refactored into a reusable pipeline pattern: Ingestion Validation Computation Storage Publishing Instead of feature-specific workers, we moved toward composable tasks. This allowed new indices or processing logic to plug into the same execution flow. 3. Observability as a First-Class Layer One of the biggest failures in the original system was visibility. We introduced: Structured logging across all services Traceable job IDs

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

One woman is dead after a Tesla with "an automated driving assistance system" engaged crashed into a home in Katy, Texas.

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

Exatamente isso que você leu. Você não precisa ser um "expert" em determinada linguagem, ou ter: 5...

The company warned about dangers of advanced AI far more than rival OpenAI.