标签：#AI

I wanted to tell my girlfriend 'I missed you today' in Farsi and have it sound like something a person would actually say, not a phrase pulled from a travel guide. Every tool I tried — Google Translate, DeepL — gave me one answer. No register. No note on whether it was too formal for a text message or too casual for a letter. Just a string of words and the implication that language has one correct answer per sentence. So I built konid: it returns three options for anything you want to say, ordered casual to formal, each with the register explained and the cultural nuance between them described. It also plays audio pronunciation through your speakers directly, using node-edge-tts — no API key, no copy-pasting into a separate tab. The interesting engineering constraint was deployment target. I wanted this to live where I already work, not in a separate browser tab I forget to use. That meant MCP. A single MCP server running at https://konid.fly.dev/mcp now serves four clients without any client-specific code: # Claude Code claude mcp add konid-ai -- npx -y konid-ai # ChatGPT (Developer mode, Actions) # endpoint: https://konid.fly.dev/mcp Cursor, VS Code Copilot, Windsurf, Zed, JetBrains, and Claude Cowork all connect the same way. The server doesn't know or care which client called it. The output structure for a query like 'I missed you today' in Japanese looks roughly like this: Option 1 (casual): 今日会いたかった Register: intimate, fine for close friends or a partner Note: dropping the subject is natural here; adding あなたに would feel stiff Option 2 (neutral): 今日、あなたのことが恋しかったです Register: polite, appropriate for someone you're close to but addressing respectfully Option 3 (formal): 本日はお会いできず、寂しく思っておりました Register: formal written Japanese; would be unusual in a personal context The nuance comparison is the part I couldn't get anywhere else. Knowing that option 3 exists and that you would almost never use it for a personal message is actually load-bearing information if you're l

AI Dev Weekly is a Thursday series where I cover the week's most important AI developer news, with my take as someone who actually uses these tools daily. OCR had a week. Mistral dropped OCR 4 with bounding boxes. Baidu open-sourced a model that beats DeepSeek-OCR. Claude got a permanent home inside Slack. And the Fable 5 ban fallout keeps getting uglier: Alibaba was apparently stealing Claude's capabilities, and even the NSA lost access to Mythos. Meanwhile, GPT-5.6 is delayed to mid-July. Let's go. 1. Mistral OCR 4: document AI gets serious Mistral launched OCR 4 this week. It's not just another OCR model. It's a full document understanding system with paragraph-level bounding boxes, confidence scores, and support for 170 languages. The specs: $4 per 1,000 pages (standard), $2 per 1,000 pages (batch) Paragraph-level bounding boxes with coordinates 72% win rate in blind tests against competitors Available on la Plateforme, Microsoft Foundry, and self-hosted for enterprise Top score on OlmOCRBench Why this matters for developers: Bounding boxes change everything. Previous OCR models gave you text. Mistral gives you text + where it is on the page. That unlocks document search, compliance systems, and any workflow where page structure matters. My take: At $4/1000 pages, this is competitive with Google Document AI ($5) and significantly cheaper than building your own pipeline. For enterprise document processing, this is probably the best option right now. For budget-conscious developers, Baidu's free alternative (see below) is worth considering. Full comparison in our Mistral vs DeepSeek vs Baidu breakdown. 2. Baidu open-sources Unlimited-OCR While Mistral went commercial, Baidu went open. Unlimited-OCR is a 3B-parameter MIT-licensed model that processes multi-page PDFs in a single inference pass. Key features: Built on DeepSeek-OCR architecture (SAM+CLIP + DeepSeek-V2 MoE decoder) Reference Sliding Window Attention for memory efficiency on long documents Tables to HTM

For a decade, "what's your frontend stack?" was a loaded question. jQuery vs. Backbone. Angular vs. React. Webpack vs. everything. The churn was exhausting, and a non-trivial chunk of our job was just keeping up. That era is quietly ending — not because we won the framework wars, but because the questions moved up a layer. The interesting problems in frontend today aren't about which library renders a list. They're about how rendering, data, and increasingly generation fit together. And AI is sitting right in the middle of that shift. The stack consolidated more than we admit Look at what most new production apps actually reach for in 2026: React or Svelte/Vue for the component model, with the framework wars settling into "pick one, they're all fine." A meta-framework — Next, Remix/React Router, SvelteKit, Nuxt — because nobody hand-rolls routing, data loading, and SSR anymore. TypeScript by default. Not a debate. The plain-JS greenfield project is now the exception. Server-first rendering (RSC, islands, streaming) as the baseline, with the client bundle treated as a cost to minimize rather than the center of the universe. The center of gravity moved back toward the server — but a smarter server that streams HTML, hydrates selectively, and treats the network boundary as a first-class design concern. The pendulum didn't swing back to 2010; it spiraled forward. What AI actually changed (and what it didn't) The hype says "AI writes the frontend now." The reality on the ground is more specific and more interesting. It collapsed the cost of the first 80%. Scaffolding a component, wiring a form, translating a Figma frame into JSX, writing the Tailwind for a layout — these used to be hours of work and are now minutes. That's real, and it's already changed how teams estimate. It did not collapse the last 20%. Accessibility edge cases, focus management, race conditions in async state, the weird Safari bug, the design-system invariant that isn't written down anywhere — this i

AI harnesses are the new vendor lock-in. To swap across harnesses easily without rewriting your app, LiteLLM launched the Lite-Harness SDK . Run your prompt across different harnesses: from lite_harness import query , AgentOptions prompt = " Fix the failing test " # Claude Code harness async for message in query ( prompt = prompt , options = AgentOptions ( harness = " claude-code " , model = " claude-opus-4-8 " ), ): print ( message ) # Codex harness async for message in query ( prompt = prompt , options = AgentOptions ( harness = " codex " , model = " gpt-5.5 " ), ): print ( message ) To enable cost controls, fallbacks, and logging, point it to your LiteLLM AI Gateway: export LITELLM_API_BASE = https://litellm.your-company.com/v1 export LITELLM_API_KEY = sk-litellm-... Engineer's Takeaway: This SDK unifies how you invoke the agents, not how they run internally. Each harness keeps its native loop and tool-calling semantics. It is perfect for A/B testing agent performance and centralizing costs, but remember it is in public beta, so custom tool injection might require extra work! The Problem I Had My team was building an internal bot to fix failing CI/CD tests. We had three engineers advocating for three different harnesses: one wanted Claude Code, another Codex, and another Pi AI. Without an abstraction layer, we would have had to maintain three forks of the same bot , with three different SDKs, three logging systems, and three ways to track costs. It would have been an impossible maintenance burden. How Lite-Harness Helped The SDK solved that exact pain point in three concrete dimensions : 1. Unified Invocation (Time Savings) Instead of maintaining three separate implementations, I had a single query() that routed to whichever harness I wanted. Switching from Claude Code to Codex was literally just changing a string in the options. This allowed us to do real A/B testing in production for two weeks without rewriting any core logic. 2. Cost Observability (The Killer

Here's a thing that surprises people the first time: an email sent through the API does not carry the signature the user set up in Gmail or Outlook. Provider signatures live in the provider's compose UI, and a programmatic send bypasses that entirely, so a message your app sends goes out with no signature at all unless you add one. The Nylas Signatures API is how you add it: store an HTML signature once, then attach it to a send by ID, and the signature gets appended to the message for you. This post covers signatures from two angles: the HTTP API your backend calls, and the nylas CLI for creating and testing one from the terminal. I work on the CLI, so the terminal commands below are the ones I reach for when I'm setting a signature up. Nylas signatures are separate from provider signatures The first thing to get straight is that these are not the user's existing signature. Nylas doesn't sync the signature configured in Gmail, Outlook, or any other provider, and that provider signature is never applied to mail sent through the API. If a message your app sends needs a sign-off, you create that signature with this API and attach it explicitly; there's no inheriting it from the connected account. That separation is deliberate, because a programmatic send is a different context from a person typing in their webmail. It does mean the responsibility is yours: a user who connects their mailbox expecting their familiar signature to appear on app-sent mail won't get it automatically. Stored signatures are grant-scoped, living at /v3/grants/{grant_id}/signatures , so each connected account has its own set, and they're HTML, so a branded sign-off with a logo and links works the same as a plain one. Create a signature Creating a signature is a POST /v3/grants/{grant_id}/signatures with a name and an HTML body . The name is for you, a label to find it by later; the body is the markup that gets appended to outbound mail. The response returns the signature with its ID, which is w

Amazon’s latest India investment comes as global tech companies race to expand AI infrastructure in the country.

To celebrate its new status as No. 1 in JD Power's initial quality ranking among mainstream automakers, Ford is opening up about the challenges it has faced in recent years, especially around its reliance on automated systems in production and design. It turns out that those automated systems were not as robust as previously assumed, […]

Your QA team built 50 automated tests in the first quarter. Leadership was thrilled. Coverage was...

IBM’s nanostack transistors could boost chip performance or energy efficiency.

kreuzcrawl began as a Rust core with bindings for ten languages. v0.3.0 ships fourteen, adds a tiered WAF-aware dispatch engine, cuts peak streaming memory from ~2.5 GB to ~20 MB, and enables SSRF defense across every outbound call path by default. It is the first release we consider API-stable. This post covers what changed, why each decision was made, and what the harder engineering problems looked like from the inside. At a glance Area v0.2.0 v0.3.0 Language bindings 10 14 (+Dart, Kotlin/Android, Swift, Zig) Peak streaming memory ~2.5 GB ~20 MB SSRF protection opt-in on by default Dispatch model static HTTP / bypass / browser tiered, signal-driven escalation WAF fingerprints — 35 across 8 vendors Fingerprint hot-reload — lock-free ( ArcSwap ), 500 ms debounce MCP tools partial 1:1 with CLI, safety-annotated CLI subcommands scrape, crawl + batch-scrape, batch-crawl, download, citations Robots / sitemap parsers engine-internal public modules API stability preview stable Four new language bindings v0.2.0 shipped Rust, Python, Node.js, Ruby, Go, Java, C#, PHP, Elixir, and WebAssembly. v0.3.0 adds Dart , Kotlin/Android , Swift , and Zig — bringing the total to fourteen. None of the per-language glue is written by hand. Every binding is generated from the Rust core by alef , our polyglot binding generator. The Dart and Kotlin/Android packages bind through the C FFI layer ( kreuzcrawl-ffi ) via dart:ffi and JNI respectively. Swift binds through clang. Zig uses @cImport against the same C header. The generation pipeline also hardened in this release: the Docker publish matrix now builds each architecture natively rather than via QEMU emulation, the Dart build no longer requires the Flutter SDK for pub.dev publishes, Swift artifactbundle checksums are injected automatically, and the Elixir/PHP/Ruby releases preserve their lock files through the source-publish step. === "Python" ```sh pip install kreuzcrawl ``` === "Node.js" ```sh npm install @xberg/kreuzcrawl ``` === "Rus

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Adding an AI feature looks deceptively easy. You sign up for an API key, paste in a prompt, and within an hour you've got a working demo that makes the whole team lean over your shoulder. Then you ship it, traffic arrives, and two things happen at once: your latency graph develops a long, ugly tail, and your monthly bill arrives with a number that makes finance schedule a meeting. The gap between "impressive demo" and "production feature" is almost entirely about cost and latency engineering. The model is the easy part. Here's how to cross that gap. First, understand what you're actually paying for Most LLM APIs bill by tokens — roughly ¾ of a word each — and they bill both directions: the tokens you send (input) and the tokens the model generates (output). Output tokens are usually several times more expensive than input tokens, which has a non-obvious consequence: a verbose prompt is cheaper than a verbose answer. This reframes optimization. People obsess over trimming their prompts while letting the model ramble for 800 tokens when 80 would do. If you want to cut cost, the highest-leverage move is almost always constraining the output : ask for JSON, ask for a single sentence, set a max_tokens ceiling, and tell the model explicitly to be terse. Latency follows the same logic. Generation is sequential — the model produces one token at a time — so output length is the single biggest driver of how long a request takes. A 50-token answer is fast almost regardless of model. A 2,000-token answer is slow even on the fastest infrastructure. Lever 1: Don't call the model when you don't have to The cheapest, fastest LLM call is the one you never make. Two techniques eliminate a startling share of traffic. Caching identical and near-identical requests. Many real-world prompts repeat — the same FAQ-style question, the same document summarized twice, the same classification of similar inputs. A cache keyed on the normalized prompt turns a repeat request into a sub-millisecond

Building a unified market intelligence platform for traders, analysts, researchers, and developers. After months of development, Jungletrade is now publicly available. The idea behind Jungletrade is simple: modern market analysis has become fragmented. Market data, indicators, analytical models, and trading signals are often distributed across multiple platforms, forcing users to maintain several subscriptions, workflows, and dashboards just to build a complete market view. We wanted to explore a different approach. 📊 The Problem Most market platforms focus on a specific layer of the analytical stack: Raw data Technical indicators Quantitative models Trading signals Each layer provides value, but users are frequently required to move between multiple tools to connect the pieces. Our goal was to create a modular ecosystem where these layers can coexist within a single platform. 🧭 The Jungletrade Ecosystem Today, JungleTrade provides four product categories: 📦 Data Structured datasets for market research and discovery. 🧠 Models Analytical frameworks designed to identify patterns and relationships within market data. 📈 Indicators Tools that transform raw information into actionable insights. ⚡ Triggers Event-driven signals designed to highlight potential market opportunities. 🔍 Built for Transparency One design decision was particularly important to us: every product should explain itself. Each product includes: Product description Key features Use cases Interpretation guidelines Methodology overview The objective is not simply to provide charts but to explain the problem being solved and how the underlying analysis works. 🔌 API First All products available through the platform are also accessible through API endpoints. Developers interested in integrating JungleTrade data into their own applications, dashboards, or research pipelines can request a demo API key through the platform. 🏗️ Architecture JungleTrade is built using a modular, service-oriented architecture des

Meta is bringing back the Facebook Creator Studio page manager, now "reimagined" as a standalone AI companion app. The new app aims to make it easier for creators to connect with their audiences and show them "exactly how to grow on Facebook," according to Meta's announcement. Meta's AI Creator Assistant is a central focus of […]

MCP + RAG: Why I Stopped Building Complex RAG Systems After MCP Changed Everything Honestly, I've spent the last four years building increasingly complex RAG systems. Chunking strategies, embedding models, vector databases, rerankers, hybrid search... you name it, I've probably wasted a weekend trying it. I had this 1,800-hour knowledge base project called Papers — six years of notes, articles, bookmarks, everything. I built RAG version after RAG version, each time thinking "this time it'll be perfect." Spoiler: It never was. Then I added MCP (Model Context Protocol) support. And I realized something that completely changed how I think about knowledge retrieval: MCP makes traditional complex RAG obsolete for most use cases. Let me explain what I learned the hard way. The RAG Trap I Was Stuck In If you've built a RAG system, you know the drill: Chunking : Should you use fixed-size, semantic, recursive, or something fancy like LLM-powered chunking? Embeddings : OpenAI text-embedding-3-large vs Cohere vs nomic-ai vs your fine-tuned model? Vector Database : Pinecone vs Weaviate vs PGVector vs Qdrant vs Chroma? Retrieval : Top-k how many? Hybrid search with keywords? Reranking? Prompt Compression : How do you fit all the retrieved chunks into the context window? I went through every iteration. At one point, my RAG system was over 2,000 lines of code. I had configurable chunkers, multiple embedding providers, caching layers, hybrid search... it was impressive. It also didn't work that well. Here's what bothered me the most: I kept throwing more complexity at the problem, but the fundamental issue never went away. I was trying to make my knowledge base smart, but AI already got smart. Why was I reimplementing all this understanding logic when the AI can already do it better than me? How MCP Changed the Game When I added MCP support to Papers, I started with the simplest possible approach: Expose two tools: search_notes and get_note_content Search is just basic text matchin

Cover: a real qa-probe run against our own stack, cropped to the summary - internal product detail withheld. We are building LightShield, a SIEM that is in active demo right now. We built most of it pair-programming with an AI coding assistant wired in over MCP - it ran our stack, read the errors, and patched its own code. For a small team that is a superpower. Until an endpoint failed. Here is the loop we kept hitting. A route returns a 500, or a 404, or an empty [] . The assistant looks at the status code and announces the cause with total confidence. Then it rewrites a handler that was never broken - because a status code is not a cause, and it had nothing else to go on. So it guessed, and it guessed wrong, and the diff made things worse. The thing is, that empty [] had at least six possible causes: the database was empty (nothing seeded) a feature flag was off a contract mismatch between the frontend and the backend an auth token that never got attached a 428 precondition a schema drift Same symptom, six different fixes. We could bisect to the real one. The AI could not - it had no ground truth, so it manufactured one. So we built qa-probe It analyzes the app, probes the live endpoints, and classifies each failure with a root cause and a fix hint. Three decoupled, cached phases: qa-probe analyze # parse source + OpenAPI -> route graph qa-probe probe # hit live endpoints (HTTP/SSE/WS), record evidence qa-probe report # classify root cause -> HTML / Markdown / JSON / AI-context # or just: qa-probe run It has adapters for FastAPI, Express, Next.js, tRPC, GraphQL, and a generic fallback, so it discovers your routes instead of you hand-listing them. The part that actually fixed our problem: every result is falsifiable Each result carries the evidence (the real request, a bounded response sample, the timing), a root cause from ~25 categories, and a calibrated confidence - high , medium , or none . When it cannot tell, it returns none instead of bluffing. No neural net

OpenAI has rolled out an upgrade for the free model you interact with the most on ChatGPT.

Over the past weeks, I’ve been sharing a series of posts that gravitate around one question: How do...

I build Voxis, an open-source Windows app that translates whatever your system is playing — a video, a game, the other side of a call — and plays the translation back as spoken voice, a few seconds behind the speaker. No subtitles, no virtual audio cable, no bot joining your meeting. The "no virtual cable" part is the bit worth writing about. Almost every system-audio tool on Windows tells you to install VB-CABLE or VoiceMeeter, or to drop a bot into your call. Voxis doesn't, for incoming audio. This post is how that capture engine works, and the sharp edges I hit building it in Python. I'll be specific about what's hard and honest about what's not mine to fix. The goal Read the exact audio the user is hearing — the post-mix system output — at 16 kHz mono, and do it without installing anything. Then stream it to a translation model and play the result back, all while the original keeps playing underneath. Three constraints fall out of that: Driverless. If it needs a reboot and a driver, it's not zero-setup. No self-feedback. The app plays translated audio into the same system mix it's capturing . Naively, it would capture its own voice and translate the translation. That has to be impossible by construction, not patched with an echo gate. Realtime-safe. Capture can't stall. If the downstream VAD or garbage collector hiccups, the WASAPI ring buffer must not overflow. WASAPI process-loopback: capturing the mix, minus yourself Windows 10 version 2004 added the ApplicationLoopback API — a way to activate an IAudioClient in loopback mode scoped to a process tree, either including only that tree or excluding it. Excluding our own process tree is exactly what constraint #2 needs: the captured mix is everything the user hears, with Voxis's own output removed. You don't get this client from the normal IMMDeviceEnumerator path. You activate it by name through ActivateAudioInterfaceAsync , passing the loopback parameters in a PROPVARIANT carrying a BLOB : params = AUDIOCLIENT_

And by viral I mean from $0 to $31. Umami told me Clew Directive got 14 visits last month. AWS told me I owed $31 for it. That works out to $2.21 a visitor, which would make it the most expensive free learning-path tool in California. Spoiler alert: 14 visitors, $31, and not a single one of them was the reason. Something was off. Here is how Amazon Q, Claude, and a few hours of reading my own code untangled it. The app turned out to be innocent. What Clew Directive is, quickly A free, stateless tool that builds you a personalized AI learning-path PDF. You take a 60-second Vibe Check, four questions about your goals and how you learn, and it maps you to free, verified resources and hands you a briefing. No accounts, no database, no paywall, nothing stored about you. It runs on Amazon Nova, which is why it costs close to nothing to operate, which is also why a $31 bill made no sense. The name is the Theseus kind of clew. A ball of thread to find your way out of the maze. Less hype, more direction. Live at clewdirective.com . The number that didn't add up Twelve visitors, 14 visits, 93% bounce, average session about a minute. Referrers from Bing, Google, Yahoo, GitHub. Visitors from the US, India, Netherlands, Egypt, Ethiopia, Singapore. Mostly crawlers stopping by to say hello. A few curious humans and a parade of bots is not a $31 month. So either every visit was doing something enormous, or the bill was never about visits at all. The dashboard lied, politely. An Amazon Q Story My cost tracker said Clew Directive was running on Claude Sonnet. Sonnet is the expensive one. Case closed, right? I opened the repo. Clew Directive does not run Sonnet. The Navigator agent runs Amazon Nova 2 Lite. Scout and Curator run Nova Micro. The IAM policy is scoped to Nova ARNs only, so a Sonnet call from these functions would come back AccessDenied. The app physically cannot bill Sonnet. The math agreed. A full learning-path generation on Nova costs about two-tenths of a cent. Fourtee