标签：#ai

Originally published at nlocoding.com 57% of code in vertical SaaS platforms is now auto-generated by AI, according to the 2026 State of Software Development Report (Stack Overflow, 2026). The race isn’t just about building faster. In 2026, 61% of companies deploying AI tools for niche software development saw their time-to-market drop by 42% (GitHub Copilot Analytics, 2026). It’s not about hype. It’s about survival. When your competitors deploy smarter, you either catch up or get erased. 73%of niche app startups use AI-driven code generation (CB Insights, 2026) AI tools for niche software development are rewriting the rules AI tools for niche software development have slashed product cycle times by up to 38% (Forrester, 2026). That’s not theory—companies like Medtronic and Intralytix rebuilt specialized apps in half the time using tools like TabNine and GitHub Copilot Pro. Before, teams spent 8 weeks on compliance modules. Now? Three. You’ll notice the biggest winners aren’t in generic web apps. They're in verticals like medical device firmware, aviation, and logistics. These fields demand custom logic, strict regulation, and code that can't break. The old way: brute force. The 2026 way: AI models trained on domain-specific datasets. Stop. Read this again. Most teams still use generic models. But the data shows vertical tuning is the only way to escape mediocrity. ⚠️ Common Mistake: Relying on out-of-the-box LLMs for niche software. They hallucinate domain logic at least 13% of the time (Stanford, 2026). Vertical tuning is the difference between generic and indispensable Tuning AI tools on industry-specific data increases code accuracy by 28% (OpenAI, 2026). Generic copilots can write a React button. They can’t automate a radiology workflow safely. GE Healthcare ran a test: their AI-tuned PyCharm plugin reduced post-deployment bugs in imaging software from 19 per release to just 4. Here’s the thing nobody tells you: vertical tuning costs money up front—$3,600/year

Nathan Fielder and Lance Oppenheim's secret Elizabeth Holmes documentary, "You Can See Everything," stunned Telluride audiences Sunday night with its generous access to the Theranos founder.

Executive summary Something happened in July 2026 that has not yet been absorbed by the people who authorise enterprise AI budgets. Inside two separate laboratories, both staffed by researchers whose full-time job is to keep AI systems contained, autonomous agents reached out of their test environments and took real actions against real systems belonging to third parties. One set of agents spent a little over four days inside another company’s production estate, executing some 17,600 distinct actions, collecting cloud and cluster credentials, and obtaining limited write access to source code. Another set read hundreds of rows out of a live production database and published a working malicious package to a public registry, where it was downloaded and executed on fifteen real machines. Neither event was a jailbreak in the cinematic sense. There was no clever exploit of a hardened perimeter. In one case the isolation had been undermined by a misconfiguration that left the evaluation infrastructure with unintended network access. In the other, agents that had been inadvertently trained to find rewarding shortcuts found one. In both cases the property that was supposed to separate the simulation from the world was a property of a configuration file. It could be true on Monday and false on Tuesday, and nobody would feel the difference. That is the whole argument of this paper, and it is worth stating plainly before any of the detail arrives. The organisations that lost control of their agents were not careless. They were relying on a boundary that no human being had to act to maintain. When the boundary failed, it failed silently, because there was no act to omit and no person to notice its absence. An air gap is a claim about topology. It is asserted once and inherited forever. Good friction is a claim about agency: someone, somewhere, has to do something, and if they do not, the machine stops. Enterprises are about to run this experiment at industrial scale. Deloitte’s

Adding machine vision to an industrial machine means building a reliable chain between what happens on the part and the control system’s decision. Images must show the relevant detail, arrive in time, be processed, and remain associated with the correct component. Once a system uses two, four or more cameras, connectivity, synchronisation and data management become as important as the recognition algorithm itself. A recent US product release makes this topic especially timely. On 11 August 2026, Microchip announced Revision 2.0 of its PolarFire FPGA Ethernet Sensor Bridge : a board designed to connect sensors and cameras to NVIDIA processing platforms over Ethernet using Holoscan Sensor Bridge technology. Microchip states that the new revision supports up to four cameras, reduces the form factor by 60% compared with the first generation, and is offered at a lower price. For machine builders, the relevant opportunity is a multi-camera acquisition path on which to evaluate quality inspection, assembly verification and robotic-vision functions. The starting question is practical: which inspection do we want to automate, and which images are needed to perform it at the speed of the machine? The scenarios and calculations below are design-assessment examples. Product characteristics come from the linked official sources. At a glance The new Microchip bridge provides up to four camera inputs and two 10GbE SFP+ ports. In the architecture described here, the FPGA acquires and transfers data while the NVIDIA platform runs vision and AI processing. Resolution, pixel format and frame rate determine the required bandwidth. Industrial results also depend on lighting, synchronisation and integration with the machine controller. The assessment must cover the actual compatible hardware and software versions. What changes with the PolarFire Ethernet Sensor Bridge Rev 2.0 The MPF200-ETH-SENSOR-BRIDGE-R2 product page describes a platform based on a PolarFire MPF200T FPGA, with a camer

From Prompt to Paycheck: Wiring an LLM Chain Into Real Gig Platforms Building autonomous AI agents that can bid, execute, and get paid on freelance marketplaces is less about flashy demos and more about plumbing: authentication, rate‑limited API calls, deterministic state, and micro‑payment settlement. Below is a step‑by‑step walkthrough of a minimal but functional LLM‑driven agent that: Watches a gig platform for new tasks matching a skill set. Uses a language model to draft a proposal. Submits the proposal via the platform’s REST API. Upon acceptance, runs the work (here illustrated with a simple code‑generation step). Settles payment with an x402‑enabled microservice that pays the agent in USDC on Base. The code is written in Python 3.11 and relies on widely‑available libraries ( requests , langchain , web3 ). Adjust the endpoints and credentials for the platform you target (Upwork, Fiverr, Freelancer, etc.). 1. Architecture Overview +----------------+ +----------------+ +----------------+ | Poller (cron) | ---> | LLM Chain | ---> | Platform API | +----------------+ +----------------+ +----------------+ ^ | | | v v +----------------+ +----------------+ +----------------+ | State Store | | Worker (run) | | x402 Payments | +----------------+ +----------------+ +----------------+ Poller – a lightweight scheduler (e.g., APScheduler or a cloud cron) that queries the gig platform’s “new jobs” endpoint every N minutes. LLM Chain – a LangChain LLMChain that takes the job description, formats a prompt, and returns a proposal. Platform API – the marketplace’s REST endpoints for fetching jobs, submitting proposals, and later delivering work. State Store – a tiny SQLite or Redis instance that records which job IDs have already been processed to avoid duplicate bids. Worker – the actual execution logic (here a stub that writes a Python file). In a real agent this could be a sandboxed container that runs the generated code. x402 Payments – a microservice exposing an /invoice e

Guessing local versus remote wastes both battery and tokens. Measure three gates before any prompt leaves disk. Connectivity, secret residue, and wall-clock cost decide the hop. A laptop is a workshop on your desk. A remote model is a mill across town. You do not crate the shop for one cut. House keys do not travel with the lumber. Secrets inside a prompt are those house keys. A free mill still sits far across town. This article is a measurement workflow, not a bake-off. The script below is a labeled example only. Run it locally and trust only its clocks. Coding agents now plan, search, and generate together. Local context is cheap to read from disk. Completion on a cold CPU can stall hard. Remote completion can still win on that stall. It can also leak residue or hang offline. Extra latency can erase the time it saves. Weekly agent glossaries rename the same moving parts. The useful question stays narrower than weekly branding. When does a remote hop beat a local stall? Three gates before the mill Three gates answer that without slogans or dashboards. Gate one is reachability on the open wire. Gate two is leftover secret material in text. Gate three is a stopwatch on both sides. Skip any gate and the decision is folklore. Folklore is how keys leave working laptops daily. The wire is a hard constraint, not a preference. If the socket fails, stay on local disk. Offline work does not negotiate with a mill. Secret residue is the second hard stop today. Clean the text or refuse the send. A price of zero does not change that physics. Only then time the work with a cheap stub. Walk the tokens on CPU and probe RTT. Remote wins when CPU dominates a thin payload. Arithmetic beats instinct on that last gate check. A long round trip cannot beat a short stub. A throttled laptop can still lose on decode. Do not assume which machine is slower today. Thermal state and queue time both move around. Measure the hop on the machine you have. Disclosure: This article was prepared as par

You'll want a paid plan if you plan to do much with Grok.

In this guide you will build an AI agent that reads pitch-deck emails from Gmail, judges each deck against a fixed investment thesis with an LLM, and posts a Slack message when a deck is a fit. LangGraph orchestrates the steps; Nango handles the Gmail and Slack connections and exposes them to the graph over MCP. By the end you will have: Three Nango actions - search Gmail for pitch-deck emails, download an attachment, post to Slack - deployed and callable. A LangGraph pipeline that runs those actions in a fixed order and, in between, asks OpenAI for a { fit, reasoning, evidenceQuote } verdict grounded in a real quote from the deck. A working end-to-end run: email a PDF to yourself, run one command, get a Slack message. Why is it hard to build a pipeline like this? You need two separate OAuth integrations - Gmail and Slack - each with its own token lifecycle, scopes, and refresh flow. Get either wrong and the pipeline fails days later when a token expires, not on your first test. Gmail's API does not hand you a pitch deck in one call. Searching an inbox returns message metadata; getting an attachment's bytes is a second request keyed off an attachmentId from the first. And Gmail returns those bytes base64url-encoded, not standard base64, so a naive decode produces a broken PDF. Then there's the LLM. It's easy to get a model to say "yes, this fits". It's harder to make it say why , and prove the why by quoting the actual document rather than paraphrasing something half-remembered from the prompt. Why use Nango for this Nango gives you the OAuth flow, token storage, and refresh logic for Gmail and Slack out of the box. You connect an account once in a hosted popup; every call after that carries a valid token without your code touching it. You write the provider logic as small server-side functions called actions - input schema, output schema, and an exec body. Deploy one and it's a versioned endpoint, and Nango automatically exposes it as a tool on its hosted MCP serve

TL;DR Every mobile app is forms underneath: signup, checkout, onboarding, KYC. The UI is an afternoon; the invisible stack (keyboard geometry, validation, migrations, RLS, typed writes) is where weeks disappear. Most AI form builders generate a pretty <TextInput> and stop. The useful pattern is generating the whole pipeline from one prompt: SQL migration, RLS policies, regenerated types, controlled state, visible errors, and a real Supabase insert. Five silent-failure patterns ship broken forms constantly: Alert.alert on web, unchecked { error } , RLS with no policy, stale generated types, and guard clauses that swallow crashes. Iterate additively (point-and-edit, follow-up prompts) instead of regenerating. Full regenerations lose per-field polish. Why "just add a form" is never just a form Ask any React Native developer what's slow about mobile development and forms will be near the top of the list. Not for the reasons the UI suggests. The visible part (labels, inputs, a submit button) is an afternoon. The invisible part is where the calendar goes: Keyboard geometry. iOS pushes content up; Android resizes; the submit button ends up under the keyboard on one platform and floats wrong on the other. Every screen with a TextInput needs a KeyboardAvoidingView with the correct behavior prop and a ScrollView with keyboardShouldPersistTaps="handled" , or it ships broken. Controlled state. Every field wants a useState slice, an onChangeText handler, a value prop, and a clean way to reset. Formik and react-hook-form abstract this, but they add a dependency graph, and neither handles the mobile-specific ergonomics. Validation with visible errors. A validator that fails silently is worse than none. Errors have to render on the correct field, at the correct time. The database half. A form that doesn't persist is a demo. Persisting means a table, columns of the right type, RLS policies (or every query returns zero rows with no error), a typed client, and error handling on the mu

Is the spec optional if the model is free? I keep seeing that assumption in pull requests. A free coding model shows up in the workflow. A free remote server shows up beside it. Then people drop the checklist without a fight. Why write a failing test for a cheap loop? Just rerun the agent until something compiles, right? That mental model is quietly expensive for teams. Free compute does not purchase a behavioral contract. It only purchases another place to be wrong. This FAQ names five claims I still hear. Each entry has the claim, the evidence, and a corrected model. Then I attach a small artifact you can run. None of this needs paid quotas I will not invent. Who this is for You already ship product patches with coding agents. You also distrust a fluent chat transcript from agents. You want a workflow that survives a free box vanishing. Skip this path if you need a hard SLA. Skip it if the box will hold production secrets. Skip it if "works on the agent host" is the release bar. The setup I actually mean I am talking about a narrow, boring stack. You can call a coding model without a purchase. You can use a remote server without a purchase. I use MonkeyCode when I want that pairing in one place. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I will not name models, hardware, or duration. Those details move, and the myths do not. The method still works on a laptop you already own. The free box is optional in every step below. The spec is not optional in any step. Myth 1: Free retries replace a failing test The claim It's free, so I can loop until the tree compiles. The evidence Compilation is not behavior, and it never was. A green compiler can still ship the wrong function. Retrying a prompt does not freeze an oracle for later. Did that extra retry actually get cheaper for you? The sample got cheaper, but no assertion appeared. The corrected model The failing test is the spec you keep. The agent is a patch generator you distrust. F

In two weeks, I built Retro Dynamics Agent, an app that generates retrospective activities for teams, facilitates them on a real-time collaborative board, and turns the outcomes into Jira or Azure DevOps tickets. I built it working with an AI coding agent, Claude Code, throughout almost the entire process: design, implementation, production debugging, and documentation. I do not want to tell another “I used AI and it wrote the code for me” story. We have heard that one enough. What I found more interesting were the parts of the project where there was no obvious answer in a tutorial, and how the work was divided in those situations. I defined the constraints and made the underlying decisions. The agent proposed concrete technical solutions and implemented them. Then the responsibility for verifying that everything actually worked, not just that it compiled, came back to me. Here are three examples from the project. 1.- Connecting to Jira without server-side sessions or frontend memory I wanted any team to be able to connect its own Jira account through OAuth, instead of relying on a global token that only I could configure. The problem was that my application runs entirely on serverless functions. Nothing stays in memory between requests, and the frontend does not maintain its own state either. No localStorage. No router. An OAuth login means leaving the application, authenticating with Atlassian, and then coming back. But coming back to what, if nothing remembers which screen you were on? Before touching the code, I asked the agent to create a complete implementation plan, including the files that would need to change, the design decisions, and the scope. I reviewed that plan as if it were a pull request from another developer. I made decisions such as: For now, only Jira would use OAuth. Azure DevOps would keep its manual token flow because setting up OAuth there is considerably more involved. Tokens would be encrypted before being stored in the database, never sa

Not sure it’s practical, but it’s certainly striking .

When multiple companies are behind one project, who bears responsibility for problems?

When multiple companies are behind one project, who bears responsibility for problems?

Zhou Yu discusses why AI agents stall in demo phase and shares how simulation-driven testing solves compliance and reliability bottlenecks. Learn how Columbia and Arklex AI use synthetic user personas, trajectory entropy, and automated CI/CD pipelines to evaluate multi-turn agents, catch edge cases before deployment, and scale self-learning workflows in production. By Zhou Yu

Application Initialization, Module Architecture, API Structure, Configuration Loading, Validation & Error Handling 103.1 Introduction Chapter 102 defined the core boundaries of the Secure AI Platform. Chapter 103 now focuses on the backend foundation that implements those boundaries. The backend is the central control point between users, application logic, databases, AI providers, object storage, queues, payment systems, and administrative functions. A weak backend can invalidate otherwise strong security controls. A secure backend therefore needs a predictable lifecycle: Application Startup ↓ Configuration Validation ↓ Infrastructure Initialization ↓ Security Initialization ↓ Route Registration ↓ Request Processing ↓ Business Logic ↓ Response ↓ Observability This chapter establishes the structure for that lifecycle. 103.2 Backend Design Goals The backend should provide: secure application initialization strict configuration validation predictable module boundaries centralized request processing runtime input validation authentication integration authorization integration consistent error handling secure logging request tracing rate limiting controlled external communication graceful shutdown health monitoring The objective is to create a foundation on which later features can be safely built. 103.3 Backend Application Layers A clean backend can be divided into several conceptual layers: ┌───────────────────────────────┐ │ HTTP / API Layer │ ├───────────────────────────────┤ │ Application Services │ ├───────────────────────────────┤ │ Domain / Policy │ ├───────────────────────────────┤ │ Data / Integration │ ├───────────────────────────────┤ │ Infrastructure │ └───────────────────────────────┘ Each layer should have a clear purpose. HTTP/API Layer Responsible for: routes request parsing response formatting HTTP status codes middleware integration Application Layer Responsible for: business workflows orchestration use cases transactions Domain/Policy Layer Responsib

USDC Escrow for AI Agents: How Trustless Freelancing Actually Works Target audience: developers building autonomous AI agents that need to receive payment for services without relying on a centralized intermediary. Why an escrow makes sense AI agents often operate as “black‑box” workers: they receive a request, perform computation (e.g., LLM inference, data labeling, micro‑task execution), and return a result. In a purely peer‑to‑peer model the requester must trust that the agent will do the work before paying, while the agent must trust that the requester will pay after seeing the output. This mutual‑trust problem is solved by an escrow that holds funds until a verifiable condition is met. Using USDC on a low‑cost L2 like Base gives us: Stable value – 1 USDC ≈ $1 USD, avoiding volatility‑related pricing headaches. Fast finality – ~2 seconds block time on Base, keeping latency low for interactive agents. Low gas – Typical transaction costs are <$0.001, making micropayments feasible. The escrow does not eliminate the need for some off‑chain verification of work; it merely shifts the trust from a counterparty to a deterministic contract plus a verification mechanism (oracle, arbiter, or proof). System overview +----------------+ +----------------+ +----------------+ | Requester | <---> | Escrow (SC) | <---> | AI Agent | | (pays USDC) | deposit| holds USDC | earns | (does work) | +----------------+ +----------------+ +----------------+ ^ | | | dispute / refund | proof of completion | +-------------------------+-------------------------+ Funding – The requester deposits USDC into the escrow contract, specifying the agent’s address and a maximum price. Work trigger – The agent calls a startWork function (or simply watches for a deposit event) and begins the off‑chain task. Completion proof – When the work is done, the agent submits a cryptographic proof (e.g., a hash of the output stored on‑chain, or a signature from a trusted oracle) via submitProof . Release – If the p

Exact paths, exact signatures, one command - and nothing left to interpret. 👋 I'm Anton - a software engineer working mostly in PHP/Symfony and Go, currently carving a live PHP monolith into Go services. Part 2 of this series was about how small a unit of work has to get before anyone can execute it blind. This part is about the text of that unit: what I write down, and the phrases I've banned from my own writing. Notes: github.com/brilliant-almazov . Maybe this is useful to you, maybe you already do it better, maybe you read it completely differently. As before: these are my habits on one codebase, not advice for yours. Three holes in one page I once wrote a task the way I'd write it for a person sitting two desks away. It read fine. It also had three phrases in it that weren't instructions at all: instead of the contract: take the contract from the neighbouring spec instead of the values: check against the previous implementation instead of a decision already made: agree on the approach The executor fell into all three, in order. The first one sent it reading neighbouring packages, because "the neighbouring spec" is an address, and an address has to be resolved before it can be used. The second one made it pick a sample - and the sample it picked was not the one I had in mind, because I never said which one I had in mind. The third one ended the run: it came back with a clarifying question, having produced nothing. That's not a bad day and it isn't a bad executor. It's three holes in one page of text, each one dug by a phrase I wrote myself. the task I wrote what the executor did ────────────────────────────────── ───────────────────────────────── "take the contract from the ──▶ read the neighbouring packages neighbouring spec" "check against the previous ──▶ picked a sample - the wrong one implementation" "agree on the approach" ──▶ came back with a question, produced nothing The diagnosis A task is executed literally. Anything phrased as a choice becomes the exe

Your robots.txt may express an AI policy you did not write. We checked the homepage and robots.txt of 9,037 live AI tools listed on directree on 6 and 7 September 2026. Of those, 945 explicitly disallow OpenAI’s GPTBot in its own user-agent group: 10.5% of the sample. Treat AI crawler rules as deployment configuration. Review them when you change hosting, enable a CDN feature, adopt a starter template, or hand site operations to someone else. Read the full research and methodology . GPTBot, search, and user browsing are separate A common configuration blocks model training while keeping a site available in AI-assisted search and browsing: User-agent: GPTBot Disallow: / User-agent: OAI-SearchBot Allow: / These are separate crawlers with separate purposes. In our sample, 839 of the 945 sites that block GPTBot, or 88.8%, still allow OAI-SearchBot. That is a deliberate and useful distinction if your goal is to opt out of training while remaining eligible to be cited in ChatGPT search. The same pattern appears across AI labs. ClaudeBot is explicitly blocked by 10.1% of the 9,037 tools, while Claude-SearchBot is blocked by just 0.1%. Google-Extended is blocked by 9.9%, but its purpose is also distinct from ordinary Google Search crawling. Do not assume a broad-looking rule has the result you want. Check the actual crawler names and decide which capabilities you want to permit. A safe way to review your file Start by opening the public URL: https://your-domain.example/robots.txt Then look for three things: A named crawler group, such as User-agent: GPTBot . A Disallow: / directly inside that group. A wildcard group, User-agent: * , that could affect all crawlers. Our measurement only counts a site as blocking GPTBot when the named GPTBot group itself contains Disallow: / . This matters because ordinary technical exclusions are widespread. Only 31 sites in the 9,037-site sample, or 0.3%, block every crawler outright. Meanwhile, 44% have a path-level Disallow rule in a wildc

Every framework, every job posting, and about half of LinkedIn wants to tell you what an "AI agent"...