标签：#ai

An AI agent that's flawless in the demo can still fall apart the first time a tool fails in production: a timeout, a network error, a response that comes back corrupted. The fix isn't a smarter model. It's testing the agent against those failures on purpose, before your users do, and hardening it one failure type at a time.

Complete preparation breeds complacency. What is seen every day no longer raises suspicion. The...

AI can generate unit tests in seconds. But how do you know whether those tests are actually useful? Most teams still rely on code coverage and pass rates to evaluate their test suites. The problem is that a test can pass, increase coverage, and still provide little or no additional confidence. We've been seeing examples where AI-generated tests: Duplicate existing coverage Depend on system time or GUID generation Access files, network resources, or environment variables Use ineffective or unnecessary mocking Add maintenance cost without improving quality Today we launched Typemock Test Review, a tool that analyzes tests during execution and identifies duplicate, fragile, ineffective, and high-maintenance tests. Instead of looking only at source code, it combines runtime behavior, code coverage, dependency analysis, assertions, and mocking patterns to determine whether a test is actually contributing value. Some of the issues it can detect: Duplicate tests Hidden external dependencies Flaky test risks Unused or stale fakes Ineffective mocking Tests that increase maintenance without increasing confidence I'm curious how other teams are dealing with the explosion of AI-generated tests. Are you reviewing AI-generated tests differently from manually written tests? Have you found good ways to measure test quality beyond coverage and pass/fail metrics?

Production vLLM is 100,000+ lines of C++, CUDA, and Python. It powers most of the industry's LLM serving — but reading it cold is brutal. So I built a study series around nano-vLLM , an open-source reimplementation of vLLM's core ideas in ~1,200 lines of pure Python. Every algorithm is visible. Every design decision is legible. It turned out to be the perfect lens for actually understanding how LLMs generate text. The result is an 11-chapter interactive guide. No ML background required — every piece of jargon is explained from scratch with analogies, diagrams, annotated source code, interactive simulators, and quizzes. What it covers: What Is LLM Inference? — tokens, autoregressive generation, Q/K/V attention, HBM vs SRAM Architecture — how 1,200 lines are organised; CPU control plane vs GPU data plane KV Cache — why storing Keys and Values turns O(N²) recomputation into O(1) lookup PagedAttention — virtual memory for the KV cache; how fragmentation wastes 60–80% of GPU memory The Scheduler — continuous batching; keeping the GPU at 95% utilisation instead of 12% Prefill vs Decode — same model, two completely different bottlenecks (compute-bound vs memory-bound) Prefix Caching — skip prefill for shared tokens; ~700ms → ~90ms TTFT Sampling Strategies — greedy, temperature, top-k, top-p, and what each does to the distribution Tensor Parallelism — splitting a model across GPUs; column/row parallel and all-reduce The Optimization Stack — FlashAttention, kernel fusion, CUDA Graphs, torch.compile Benchmarks — measuring honestly; why nano-vLLM matches vLLM on core throughput Each chapter is fully self-contained and interactive. A few of the simulators I'm most happy with: a PagedAttention block allocator you can fill up and watch fragment, a live scheduler you step through token by token, and a sampling playground where you reshape the probability distribution with sliders and sample from it. 🔗 Read the full series: https://ashwing.github.io/vllm-guide/ It's free and open.

If you give an LLM agent a table of A/B variants and ask "which one should we send next?", it will confidently pick the one with the highest conversion rate. That feels right. It is often wrong. The model has no concept of sample size , exploration , or regret . It pattern-matches "biggest number = winner" and moves on. For a one-off question, fine. But inside an agent loop that picks a variant on every request — email subject lines, ad copy, model routing, recommendation ranking — that naïve pick quietly accumulates regret and starves the options it never gave a fair chance. The fix isn't a better prompt. It's to not ask the LLM to do the math at all. Route the decision to a real bandit algorithm and let the model do what it's good at (orchestration, language) while a deterministic solver does what it's good at (the optimization). This post is a copy-paste demo you can run in your terminal right now , no signup, no API key. I'll use OraClaw — a deterministic decision-intelligence MCP server — but the point stands regardless of tool: stop letting the model guess at math it can verify. The trap, concretely Here's a realistic state mid-experiment. Three subject lines, different amounts of traffic: Variant Pulls Rewards (conversions) Raw rate A 120 18 15.0% B 80 17 21.3% C 15 4 26.7% Ask an LLM "which should we send next?" and you'll usually get B — it has the best rate among the well-tested variants, and C "only has 15 samples, too noisy to trust." That reasoning sounds responsible. It's exactly backwards. With only 15 pulls, C is under-explored — we don't actually know it's worse, and the cost of finding out is tiny. A bandit's whole job is to weigh that uncertainty instead of hand-waving it away. Let's get a real answer. Run it yourself: the no-key REST endpoint (60 seconds) OraClaw exposes a free, no-auth REST endpoint. Paste this into your terminal — nothing to install, nothing to sign up for: curl -s -X POST https://oraclaw-api.onrender.com/api/v1/optimize/bandit

Andrej Karpathy, OpenAI co-founder and former Tesla AI director, called Claude Tag the third major...

One-time passcodes are everywhere: sign up for a service, log in from a new device, confirm an action, and a six-digit code lands in your email. A human glances at it and types it in. An automated flow, a signup script, an end-to-end test, or an AI agent connecting to a third-party service, can't glance at anything. It has to pull the code out of the mailbox programmatically, and that's a surprisingly fiddly job: the code arrives seconds after a trigger, it's buried in a templated email, and every sender formats it differently. This post covers extracting verification codes from two angles: the nylas CLI , which does it for you in one command, and the Email API pattern you build when it's part of a larger flow. I work on the CLI, so the terminal commands below are the ones I reach for when I just need the code. Two ways to get the code There are two paths depending on what you're building. For terminal workflows, local testing, or scripting a login, the CLI has a dedicated nylas otp command that finds the latest code in a mailbox and hands it to you. For an application or an agent that reacts to incoming mail, you build the extraction into your own flow: catch the message when it arrives, pull the body, and parse the code out. The difference is who drives. The CLI is pull-based: you ask for the latest code when you need it. The API pattern is push-based: a webhook tells you a message arrived, and your code extracts the value as part of handling it. Both end at the same place, a string of digits you feed into whatever's waiting for it, but the CLI is the fast path for a developer and the API pattern is the durable path for a product. In practice you use both: the CLI to learn which sender and code format you're dealing with during development, then that same understanding baked into the application pattern for production. Grab the latest code from the CLI When you've just triggered a code and want it now, nylas otp get finds the most recent one in your default accoun

Every Nylas request you make on a user's behalf needs one thing first: their permission. Before you can list a mailbox, send on someone's behalf, or read a calendar, the user has to authorize your application through their provider, and that authorization is what's called a grant. Doing the OAuth dance yourself means registering with Google and Microsoft separately, handling each provider's consent screen, token exchange, and refresh quirks. Hosted OAuth collapses that into one flow that works the same across every provider. This post walks through connecting an account from two angles: the HTTP API your web app uses in production, and the nylas CLI for connecting a test account from the terminal. I work on the CLI, so the terminal commands below are the ones I reach for when I need a grant to develop against. What a grant is A grant is an authenticated connection to a single user's account. When a user authorizes your application, Nylas stores the connection and hands you a grant_id , a stable identifier you pass on every subsequent request to act on that user's email, calendar, or contacts. The grant is the unit of access: one user who connected one mailbox is one grant, and everything you build addresses /v3/grants/{grant_id}/... . Keep two credentials distinct here. Your API key authenticates your application to Nylas and goes in the Authorization header on every request; the grant_id identifies which connected user that request acts on. The API key is yours and stays on your backend, while a grant_id is minted per user when they connect. The grant is also where provider differences disappear. A Gmail grant and a Microsoft grant have different OAuth scopes and token mechanics underneath, but once connected, both are just a grant_id you use the same way. That's the point of hosted OAuth: you run one flow, the user picks their provider, and you get back the same kind of identifier regardless of who hosts the mailbox. Hosted OAuth supports Google, Microsoft, Yahoo,

Line AI Chatbot In Production: A CTO's Honest Breakdown Three months ago I was staring at our infrastructure bill wondering where the hell our runway went. We'd been running a customer-facing chatbot powered by a popular "enterprise" AI provider, and the cost curve looked like a hockey stick in the wrong direction. Every new sign-up bled money. I knew we had to make a change before our next board meeting, but I also couldn't afford a six-week migration that would tank our product velocity. What I found surprised me. After running the numbers, testing 184 models through Global API, and stress-testing everything at scale, I cut our inference costs by more than half without touching quality. This isn't a theoretical comparison from a vendor whitepaper. These are the real numbers from my production stack, with my actual users, in my actual platform. If you're a CTO weighing your options for 2026, here's everything I wish someone had told me before I started. Why The Line AI Chatbot Approach Matters Now Most chatbot guides treat AI integration like a toy problem. Send a prompt, get a response, ship the demo. That's fine for a hackathon, but it's not how you run a production system. The questions I care about are different: What's my cost per active user? How do I avoid vendor lock-in? Where's the single point of failure? How fast can I iterate on model choice when something better drops next Tuesday? The Line AI Chatbot framework flips the typical approach. Instead of treating the model as a black box you can't replace, you build a thin abstraction layer over a model-agnostic API. That single architectural decision is what unlocked every other win I describe below. If you're not thinking about model portability on day one, you're going to pay for it later. I learned this the hard way. In 2026, the market has matured to a point where you genuinely have 184 models to choose from, with input prices ranging from $0.01 to $3.50 per million tokens. That's not a marketing line.

Introduction "Build and run agents you can see, understand, and trust." This is article #104 in the Open Source Project of the Day series. Today's project is AgentScope 2.0 — Alibaba DAMO Academy's open-source production-ready agent framework. The agent framework space is crowded. LangChain centers on chain-based orchestration. AutoGen centers on multi-agent conversation. CrewAI centers on role-based collaboration. AgentScope's differentiation is in its design philosophy: when LLM reasoning is strong enough, the framework should step back rather than constraining the model's decision space with rigid pipelines. AgentScope 2.0 adds the production infrastructure that philosophy requires: event system, permission controls, multi-tenant isolation, sandbox execution, middleware hooks. The goal is not a demo that runs — it's a system that ships. What You'll Learn AgentScope 2.0's design philosophy: why "model-led" over "fixed pipeline" The five core systems: Event / Permission / Multi-tenancy / Workspace / Middleware Agent Team pattern: how the Leader-Worker architecture handles complex tasks Permission system fine-grained control: tool call approval and boundary configuration Positioning differences vs. LangChain and AutoGen The full ecosystem: AgentScope Runtime, ReMe, OpenJudge, Trinity-RFT Prerequisites Familiarity with LLM agent concepts (tool use, reasoning loop) Basic Python async programming Experience with LangChain or AutoGen helps with positioning comparison Project Background What Is AgentScope? AgentScope 2.0 is a production-ready agent framework — "an agent development platform with essential abstractions, designed to work with rising model capability, with built-in production support." The core problem it addresses: traditional agent frameworks constrain LLMs with rigid pipelines and opinionated prompt templates. As LLM reasoning capability has improved rapidly, that constraint has become a bottleneck. AgentScope shifts to "letting the model's native reason

Dein bestes Google-Ranking ist wertlos, wenn die Antwort schon vor dem Klick gegeben wurde. Genau das passiert gerade: Nutzer fragen ChatGPT, Claude oder Perplexity – und bekommen eine fertige Antwort mit drei, vier zitierten Quellen. Bist du nicht darunter, existierst du in diesem Moment nicht. Kein Ranking, kein Klick, keine zweite Chance. Die Disziplin, die das adressiert, heißt Generative Engine Optimization (GEO) . Und sie ist – anders als der Marketing-Lärm vermuten lässt – zu großen Teilen ein Engineering-Problem. Crawler-Zugang, Rendering, strukturierte Daten. Lauter Dinge, über die ein Entwickler entscheidet, nicht das Content-Team. SEO optimiert auf den Klick. GEO optimiert auf das Zitat. Der Unterschied ist nicht kosmetisch. Klassisches SEO will, dass du auf Platz eins rankst, damit jemand klickt. GEO will, dass ein Sprachmodell deinen Absatz wörtlich in seine Antwort übernimmt – inklusive Quellenangabe. Der Klick ist nur noch Bonus. Daraus folgt ein anderer Tech-Stack an Signalen: Aspekt Klassisches SEO GEO / KI-Sichtbarkeit Ziel Top-10 in Google Zitat in ChatGPT, Claude, Perplexity Relevante Bots Googlebot, Bingbot GPTBot, ClaudeBot, PerplexityBot Index-Hinweis sitemap.xml llms.txt + sitemap.xml Strukturierte Daten Rich Snippets Entity-Linking ( Organization , sameAs , @graph ) Rendering Google rendert JS (verzögert) viele KI-Bots rendern kein JS → SSR Pflicht Erfolgskontrolle Search Console, Rank-Tracker Citation- & Mention-Tracking in LLMs Die Hebel überschneiden sich – sauberes HTML, schnelle Antwortzeiten, valides Markup helfen beidem. Aber die Bots, die Index-Signale und die Erfolgskontrolle sind eigenständig. Wer GEO als „SEO mit neuem Namen" abtut, übersieht genau die Stellen, an denen es klemmt. Schritt 1: Lass die Bots überhaupt rein Bevor du über Content-Qualität nachdenkst, klär die banale Frage: Kommt der Crawler durch? Erstaunlich oft lautet die Antwort nein – und niemand merkt es, weil ein Browser die Seite ja problemlos lädt. Die drei Use

You don't need a 5-person engineering team to run like one. Here's how small businesses are cutting manual work — without the overhead. The Problem Nobody Talks About Openly You're running a business. You have leads coming in from your website, follow-up emails to send, invoices to track, onboarding tasks to assign — and somehow, you're still doing most of it manually. You've probably heard the advice: "Just hire a developer." But a full-time developer costs $60,000–$120,000/year in the US. A dev team? Multiply that by four. For a growing small business, that's not an option yet. Here's what nobody tells you: you don't need a full dev team to automate 80% of your operations. You need the right tools — and someone who knows how to connect them. What "Workflow Automation" Actually Means (No Jargon) Workflow automation is just this: if X happens, do Y automatically — without you touching it. Some real examples: A new lead fills out your form → they get an automated welcome email + a task is created for your sales rep An invoice is marked paid → a receipt is sent + your spreadsheet updates + a Slack message goes to your finance channel A support ticket comes in → it's categorized, assigned, and a reply is sent based on the topic You already know these need to happen. Automation just removes you as the middleman. The Modern Stack: 4 Tools That Do 80% of the Work 1. Zapier / Make (formerly Integromat) These are no-code automation platforms. Think of them as the "if this, then that" engine for your apps. Connect 5,000+ apps (Gmail, Shopify, Notion, Slack, HubSpot, etc.) Build multi-step automations visually — no coding needed Best for: Email triggers, form responses, basic data sync Cost: Free tier available; paid from ~$20/month 2. Salesforce (with OmniStudio / Flow) If your business is scaling or you're in B2B sales, Salesforce isn't just a CRM — it's an automation engine. Flow Builder lets you automate record updates, approvals, emails, and task assignments visually Omn

If you've ever sent a transaction on Solana and wondered why it landed instantly one time and struggled another, you're not alone. Solana is incredibly fast, but how your transaction enters the network matters just as much as what you're sending. In this article, we'll break down Solana transaction processing in plain English — no developer jargon — and explain why landing services like Lunar Lander and Astralane can dramatically improve speed and reliability. The Big Picture: How Solana Handles Transactions At a high level, Solana works like this: You submit a transaction The network decides which transactions get processed first A validator includes your transaction in a block The transaction is finalized on-chain The key detail most users don't see is step #2 — how Solana decides which transactions get priority when the network is busy. That decision is driven by something called Stake-Weighted Quality of Service (QoS) . Stake-Weighted QoS (Explained Like You're Not a Developer) Solana has a built-in traffic management system. Think of it like traffic control for a highway. A Simple Analogy Imagine a highway with two lanes: 🚗 Fast lane (priority access) 🚙 Regular lane (everyone else) Solana prioritizes transaction traffic based on stake, meaning traffic originating from or routed through high-stake validators is more likely to be processed during congestion. Why? Because validators that stake SOL are financially invested in keeping the network healthy. Giving them priority helps protect Solana from spam and overload. What This Means for You Transactions that enter Solana through stake-backed paths have a much higher chance of landing quickly Transactions that enter through generic or overloaded RPCs compete for a smaller slice of capacity During congestion, non-priority transactions are more likely to be delayed or dropped This is the core idea behind Solana's stake-weighted QoS system. Where Transactions Usually Go Wrong Most wallets and apps send transactions t

Just finished reading Saha et al. arXiv 2506.07001 on adversarial paraphrasing for AI detector evasion. Key claim: detector-guided paraphrasing with RoBERTa as reward reduces TPR by 87.88 percent across Binoculars, Fast-DetectGPT, Ghostbuster, RADAR, GPTZero. Universal, training-free. What surprised me: the approach works even on detectors that were trained with adversarial examples baked in. Suggests the discriminator signal is fundamentally narrower than the generator space. Open questions: Does this generalize to detectors using surprisal variance (DivEye 2509.18880)? Multi-LLM round-robin generation: would mixing 3-4 models in pipeline give even more headroom? Token-level homoglyph substitution (SilverSpeak) is trivially detectable via Unicode normalization, but adversarial paraphrasing leaves no such forensic signal.

One of the most powerful upgrades you can give any AI developer Introduction AI coding assistants are dazzling on a single file and surprisingly lost on a large one. Point a capable agent at a mature, multi-package codebase and you watch the same pattern every session: it greps for a symbol, opens a dozen files to work out how they fit together, and burns a large slice of its context window simply rediscovering the shape of the system before it can do any actual work. That orientation phase — the crawling, the grepping, the file-by-file reconstruction of structure the codebase already encodes — is the single biggest waste of tokens in most AI-assisted workflows. And it repeats every session, because nothing persists. The fix is straightforward: give the agent a map. Model your codebase as a knowledge graph and let the agent query the map instead of crawling the territory. This article explains what that looks like, why it works, and what it actually finds when you run it on a real system. TL;DR A code-graph should map your architecture not just your code. Converting grep to graph minimises token usage and saves you time. Find bugs, security mistakes, and omissions in your codebase in seconds. Plan upgrades quickly and with far more accuracy. Using deep-memory's vocabulary simplifies usage for your AI. Use deep-memory free. Check out the full example code-graph-guide.md What is a code-graph A code-graph is a graph database representation of your system. I use the word system and not code deliberately — the real power comes from driving architectural insights, not just building a faster file search. Code graphs are becoming popular. There are some impressive repositories where the author has scripted a process to mirror a codebase into a graph DB, capturing files, imports, and call relationships. That approach is useful for dependency visualisation. But mirroring syntax only scratches the surface. What separates a useful code-graph from an elaborate directory listing

I remember the exact moment my first AI side project died. It was 3 AM, I had just spent two full weeks building an elaborate RAG pipeline with vector databases, custom embeddings, and a fine-tuned model—all for a tool that would "revolutionize how developers read documentation." I hadn't written a single line of user-facing code. I hadn't even validated if anyone wanted it. And when I finally deployed it to a hobby server, the cost of hosting the model alone was $200/month. I killed the project before anyone ever visited the URL. That was three months ago. Since then, I've shipped three AI side projects that actually have users. Not millions—but real people who use them daily. Two of them even cover their own hosting costs now. The difference? I stopped trying to build the perfect AI infrastructure and started shipping the stupidest thing that could work. Here's what I learned from those three MVPs, and how you can break out of the "AI side project graveyard" too. The Trap: Thinking You Need to Build Everything The biggest lie in the AI side project space is that you need to own the stack. Every tutorial screams "self-host Llama 3," "set up your own vector database," "build a custom agent framework." That's great for learning, but it's death for shipping. For my second project—a tool that automatically generates commit messages from diffs—I spent exactly one evening. I used the OpenAI API directly, with no caching, no streaming, no error handling. Here's the core of it: import openai import subprocess def get_diff (): result = subprocess . run ([ " git " , " diff " , " --cached " ], capture_output = True , text = True ) return result . stdout def generate_commit_message ( diff ): response = openai . chat . completions . create ( model = " gpt-3.5-turbo " , messages = [ { " role " : " system " , " content " : " Write a concise git commit message summarizing the changes. " }, { " role " : " user " , " content " : diff } ] ) return response . choices [ 0 ]. message .

Every developer using Cursor , Claude Code , Windsurf , or GitHub Copilot knows this exact frustration: You are building a cutting-edge Angular 22 application. You ask your AI coding assistant to spin up a dynamic form, a lazy-loaded list, or an asynchronous data card. Instead of leveraging modern fine-grained reactive Signals, optimized native block control flows, or proper SSR hydration hooks, the AI drops an unoptimized pile of legacy tech debt full of NgModules , *ngIf , *ngFor , and raw RxJS BehaviorSubjects . The LLM Training Paradox Why does this happen? Large Language Models are trained on historical code datasets. Statistically, more than 90% of the public Angular repositories and StackOverflow threads on the internet represent older paradigms. Left to their own devices, agents default to the statistical average of their training data. They literally default to the past. The Fix: angular22-agent-skills To solve this, I built a public, open-source repository of custom instruction bundles and system guardrails leveraging the new skills.sh tool standard. By injecting this verified context directly into your development environment, you force your local AI agents to bypass their training averages and write pristine, optimized, modern Angular 22 syntax every single time. 👉 Check out the repo here: https://github.com/PavanAnguluri/angular22-agent-skills 🔍 The Difference: Before vs. After To understand why these guardrails are necessary, look at what an AI agent writes out of the box versus what it writes once you apply the angular22-agent-skills harness. 🚫 What AI Agents Generate by Default (Legacy) // The AI falls back to old decorators and heavy RxJS boilerplate for standard state import { Component , Input , OnInit } from ' @angular/core ' ; import { BehaviorSubject } from ' rxjs ' ; @ Component ({ selector : ' app-user-profile ' , template : ` <div *ngIf="visible"> <h3>{{ firstName }} {{ lastName }}</h3> <div *ngFor="let item of items"> {{ item.name }} </div>

Over the last year or so, I've noticed more developers talking about open source API tools. Not just...

I'm Dr. Mohammad Reza Beheshti, Founder of CyberSiARA. I hold a PhD in Electronic Engineering and Artificial Intelligence and have over 15 years of experience in cybersecurity research and innovation. My passion has always been solving complex security challenges through technology. This journey led me to found CyberSiARA, where we're developing AI-powered bot protection and human verification solutions to help organizations defend against increasingly sophisticated cyber threats. I enjoy combining academic research with practical engineering to create technologies that are both innovative and effective in the real world. Through this blog, I share insights from my research, product development, and experiences building a cybersecurity company, with the aim of helping developers and security professionals stay ahead of emerging threats. I'm always keen to learn, collaborate, and contribute to the global developer and cybersecurity communities.

The boat-agent stack here runs on a prime directive: if there's something usable out there, improve it; build our own only as a last resort. So when we needed a SignalK MCP server, the honest first move wasn't to write one — it was to evaluate the one that already exists. VesselSense/signalk-mcp-server (TypeScript, MIT) is good work. It exposes SignalK to an agent through a single execute_code tool: the model writes JavaScript, the server runs it in a sandboxed V8 isolate ( isolated-vm ), and only the result comes back. Its README claims a 90–96% token reduction versus traditional named MCP tools — 2,000 tokens down to 120 for a vessel-state query, 13,000 down to 300 for a multi-call workflow. Those numbers are plausible, and they line up with the broader industry result that code execution beats tool-calling on token efficiency for complex multi-step work. We read it, ran the numbers against our own agent, and kept our discrete-named-tool signalk-mcp anyway — then harvested three of VesselSense's ideas into our roadmap. This post is that evaluation: the two philosophies, why the obvious-sounding win doesn't bind for a voice-first agent, and a decision framework you can reuse before you adopt-or-build your own MCP server. This is a design-reasoning post, not a debugging saga, but it maps to the same arc: a question, the dead-end that looks like an obvious yes, and the call that actually held. The question Two SignalK MCP servers, two genuinely different designs: VesselSense/signalk-mcp-server sailingnaturali/signalk-mcp ───────────────────────────── ─────────────────────────── one tool: execute_code discrete named tools: → agent writes JavaScript read_sensor(path) → runs in a V8 isolate battery_state(bank) → queries SignalK, returns depth_state() only the result get_route() get_local_time() TypeScript / Node + isolated-vm list_paths(prefix) claims 90–96% fewer tokens get_active_alarms() Python, end-to-end The adopt-vs-keep question: does the token-efficiency win bin