标签：#ai

À trois semaines de l'échéance du 30 septembre, beaucoup d'équipes de l'UEMOA écrivent du code de paiement dans l'urgence. La BCEAO a reporté à cette date la connexion à la plateforme PI-SPI pour les banques, les établissements de monnaie électronique et les établissements de paiement. Fin juin, 80 participants étaient connectés et 74 institutions encore en phase de test réel. Le Sénégal mène l'Union avec 20 institutions autorisées au 2 avril. PI-SPI règle l'interopérabilité entre institutions. Il ne règle pas ce que fait votre code quand un appel à Wave ou à Orange Money expire et que votre file de jobs le rejoue. C'est de ça que parle cet article, parce que c'est le bug que l'urgence produit et qu'on ne voit qu'en production, sur l'argent de quelqu'un d'autre. Le scénario, en trois lignes Un client valide sa commande. Vous appelez l'API du fournisseur. La requête met trente secondes, votre client HTTP abandonne, le job échoue, Laravel le relance. Le paiement est-il passé une fois ou deux ? La réponse dépend entièrement du fournisseur, et les trois que j'ai intégrés répondent différemment. MTN MoMo : une clé, et un piège dans sa réponse MTN est le seul des trois à fournir une vraie clé d'idempotence. L'en-tête X-Reference-Id sur POST /collection/v1_0/requesttopay , qui doit être un UUID. Si vous y mettez votre numéro de commande, l'API refuse sans expliquer pourquoi. Rejouez la même référence et MTN ne rejoue pas le paiement. Il répond : HTTP / 1.1 409 Conflict {"code": "RESOURCE_ALREADY_EXIST"} Le piège est là. Beaucoup de code PHP traite tout ce qui n'est pas 2xx comme un échec, et mon driver faisait pareil. Suivez alors le chemin complet : l'appelant subit un timeout, rejoue avec la même clé, reçoit un 409, voit une exception, conclut que rien n'est passé, et repart avec une nouvelle référence. Cette nouvelle référence est une nouvelle demande de paiement. Le client reçoit un second prompt. S'il valide, il paie deux fois. La clé a parfaitement fonctionné. C'est

Three parties describe the same roof and none of them can read the others' description. The town assessor's record says asphalt shingle, average condition, year built 1962 . A vision model looking at a satellite tile and a street-level photo says gable, moderate pitch, two planes, one chimney interrupting the north field . An estimator standing in the driveway says twenty-two squares, stick-framed, ring-shank sheathing nails, probably plywood over the original boards . All three are describing one assembly. There is no shared vocabulary between them, and the moment a fourth party arrives — a crew with a crane, or eventually a robot — the problem gets worse, not better. So the question: can a single grammar carry a roof from the assessor's record to a crew's cut plan to a machine's disassembly sequence without any party translating? ML Systems' bet is the Collective Ontology . What follows is the argument for it, made on the one assembly where it is easiest to check — and the admission at the end that it has not been checked. An assembly is a stack, and a stack has a direction A house is not one object. It is a set of assemblies — roof, walls, floors, foundation — and each assembly is a stack of layers applied in a known order. A roof is rafters, then sheathing over them, then underlayment, then shingles last. Each layer was fastened through the layer beneath it. The fastening is what makes four layers one structure, and it is also what destroys them when the structure is taken apart in the wrong direction. Two scales, and they run opposite ways: Scale Direction Order The building — between assemblies Reverse of construction Roof → walls → floors → foundation An assembly — between layers Inside out Innermost layer → outermost layer This is the thing most easily got backwards. A tear-off runs reverse order within the assembly — shingles first, because shingles went on last — and that is precisely the order that destroys everything, because every layer is broken throug

Google Analytics 4 has added a native way to identify traffic from recognized AI assistants. From May 13, 2026, GA4's Default Channel Group includes an AI Assistant channel, giving qualifying visits a dedicated medium, channel grouping and campaign value. The change makes it easier for marketers to see traffic from tools such as ChatGPT, Gemini, Deepseek, Copilot and Grok in standard acquisition reporting, rather than relying solely on custom rules. The new classification is important because AI assistants can increasingly influence how people discover websites. Until now, teams that wanted to isolate this traffic often had to build and maintain regex-based custom channel definitions. GA4's native grouping reduces that work for recognized sources, but it is not a complete solution to every AI-related attribution problem. What GA4's AI Assistant measurement changes Google documents the new channel in its GA4 Default Channel Group definitions . For visits GA4 recognizes as coming from an AI assistant, the platform applies three specific traffic-dimension values: Traffic dimension Earlier default reporting approach AI Assistant measurement from May 13, 2026 Medium No native ai-assistant assignment described ai-assistant Default channel group No AI Assistant channel described AI Assistant Campaign No native (ai-assistant) assignment described (ai-assistant) This means the relevant traffic can be examined through GA4's standard reports, including Acquisition reports and related channel dimensions. A marketing team can use the new channel to separate recognized AI assistant visits from Direct, Organic Search, Referral and other acquisition sources without first recreating source-recognition logic in a custom channel group. The scope matters. GA4 describes the channel as covering visits from recognized AI assistants, not every website session influenced by an AI tool. The classification depends on the information available when a visitor reaches the site. Why the change im

A "45% faster PR review" number is a great headline. The question is whether it means anything, because the post announcing it gives you no way to check. Atlassian's blog says Rovo Dev, their AI code reviewer, cut PR cycle time by up to 45% internally and 32% for customers. That's it. No methodology, no baseline definition, no sample, no how-the-slices-were-chosen. Just a number and a graph. That's not a knock on the product. It's a gap in the evidence. And the gap is exactly where this claim goes wrong when teams try to reproduce it. The first thing to ask is: 45% off what baseline? If your reference is "PRs that sat in the queue for three days waiting on a human nobody paged," then moving baseline checks to an AI that answers in minutes is going to look incredible no matter how good the reviews are. That's a queue problem being measured as a review problem. Once the backlog is gone, the 45% doesn't hold. Second, a single cycle-time aggregate hides the tail. A mean drops fast when the AI eats the easy set: the small, low-risk, well-documented diffs that a reviewer was already going to green-light quickly. The expensive PRs, the big architectural ones with real design risk, those still need human time and they still dominate the tail. Report p50 vs p95 and you'll see where the win actually sits. Third, and least glamorous: reviewer pool and busy-time matter. If the human reviewers on the measured team changed, or the team slowed its own review culture at the same time the tool shipped, you're attributing a confound to the tool. None of this is hard to fix. If you're a vendor publishing a PR-time win, or a buyer trying to validate one, run this and show the raw numbers: Pick a fixed window (two weeks feels about right, nothing shorter). Keep the reviewer pool fixed. No new hires, no reorgs. Split the window by PR size and by risk level, not just by the whole sample. Report median and p95, not just the mean. State what "baseline" means before the tool, and measure aga

Microsoft published the rare thing in this space: a first-party, attributed throughput number for AI code review. PRAssistant ran across 5,000 internal repos, covered 90%+ of PRs and 600K+ pull requests a month, and measured a 10–20% median improvement in PR completion time. Then it shipped externally as GitHub Copilot code review. That's worth slowing down on, because "median PR completion time" does real work in that sentence and people keep reading past it. What that metric captures is a throughput proxy. Code review is a queue: reviewers pick up a PR, read it, and merge it. Automation that screens for the obvious problems and unblocks trivial merges moves a strict majority of the queue much faster, which is exactly what a median improvement shows. It does not measure that the code is better. A reviewer who trusts the bot and merges faster, on code no human actually reasoned about, gets a faster merge and a worse product. The number can't tell the difference. That's not a critique of Microsoft. It's a caution for anyone who quotes the 10–20% as an argument that the tool catches more bugs. It changes when work starts, not necessarily what lands. The other part worth keeping is the scale language. 90%+ of PRs and 600K a month means they ran this only where it could keep up. PRAssistant is free to be picky about what it reviews because Microsoft's review volume is gigantic and the automation is boring; it wins by being available on almost everything. Then the rollout became Copilot code review, so the number is best read as a floor. What it did at Microsoft's volume is not a ceiling for what a smaller team should expect. The practical habit, if your team is about to adopt one of these: track review throughput and signal quality separately. Watch median time-to-merge on your side, sure. But also watch how many merged PRs came back with a regression or a follow-up fix six weeks later. One tracks speed, the other tracks whether the speed is real. Both are easy to measu

If you want the Rails implementation version of this, Webhooks in Rails goes deeper on verification, durable receipt, idempotency, retries, jobs, testing, and provider-specific behavior, and includes an Agent Companion for repo-aware coding agents. Already have webhook code in an existing Rails application? The free Webhook Architecture Checkup is a repo-aware prompt for tracing the flow you already have and finding the important gaps. Webhook endpoints always seem simple when you build the first version. Add a route, create a controller action, parse some JSON, update a record and return 200 . Pretty standard Rails stuff. Then the real requirements start showing up. You need to verify that Stripe or GitHub actually sent the request. The provider wants a response quickly, so the useful work moves into a background job. The same event arrives twice. A worker dies after doing half of the work. Two related events get processed at the same time. Another event shows up out of order. At some point, that little controller action has picked up a surprising amount of infrastructure around it. Nobody starts by saying, "I need a distributed system for this webhook." You normally get there one completely reasonable requirement at a time. It is still a small system, of course. We are not building Kafka and twelve services here. But once a webhook is production-ready, you have an external trust boundary, durable ingress, asynchronous workers, duplicate delivery, retries, concurrency, ordering problems and a handful of failure states that all need to agree with each other. That is the part of webhooks I find interesting. A tiny HTTP endpoint becomes a pretty good microcosm of a much larger distributed system. First, can you trust the request? A webhook is public ingress into your application, so before doing anything useful with the payload, you need to answer the obvious security question: did the provider actually send this? Most providers solve this with a shared secret and a s

Listen Labs walked away from a signed Series C term sheet from Menlo Ventures, sources say.

...but let me say this right away: coding was never the most valuable part of software...

Paul Christiano, an influential AI researcher focused on alignment, is joining the OpenAI Foundation as a member of its board.

Age is a major dividing line in reported ChatGPT adoption in the United States. In Pew Research Center's 2025 snapshot, 58% of adults aged 18 to 29 said they had ever used ChatGPT, compared with 41% of those aged 30 to 49, 25% of adults aged 50 to 64, and 10% of those 65 and older. The overall figure was 34%. The result matters because an overall adoption statistic can obscure very different levels of familiarity within a workforce or customer base. Businesses planning AI-enabled processes should not assume that employees or customers begin with the same experience of conversational AI. The relevant question is not whether a national usage figure is rising, but where familiarity is concentrated and where practical support may be needed. Pew's chart on ChatGPT use by age documents the 2023 through 2025 trend and shows that use rose across every age group. It also shows that the gap between younger and older adults remained substantial as adoption expanded. The age gap is persistent, even as use grows Pew's figures show broad growth from 2023 to 2025, rather than growth limited to younger adults. But the starting points and adoption levels differ sharply. In 2025, adults aged 18 to 29 were nearly six times as likely as adults 65 and older to report ever having used ChatGPT. Age group 2023 2024 2025 18 to 29 33% 43% 58% 30 to 49 21% 27% 41% 50 to 64 13% 17% 25% 65 and older 4% 6% 10% All adults 18% 23% 34% The measure is important to interpret correctly. It captures whether respondents had ever used ChatGPT , not how frequently they use it, how skilled they are at prompting, whether they use it at work, or whether they trust its output. Age is therefore a useful signal for planning, but it is not a substitute for asking people about their actual needs, confidence and tasks. Pew's more recent 2026 materials expanded the scope to ask about other chatbots as well. Adoption levels were higher across age groups under that broader framing, so businesses should avoid treating

Massachusetts has become the third state in as many months to slap new restrictions on data center development.

Suno's new v6 AI music model is its first made with support from the record industry. Suno's Jack Brody told The Verge that v6 was "trained from the ground up, with a new set of data that does not include the same data that our previous models were trained on." The data includes content licensed […]

ChatGPT had an estimated 67.7 million monthly active users in the United States , according to eMarketer market data. Separately, Pew Research Center found that 34% of US adults had ever used ChatGPT as of June 2025, up from 18% in July 2023. Both figures point to substantial public exposure to the service, but they should not be combined or treated as interchangeable measures of adoption. The difference matters for anyone using AI usage data to inform product, marketing, or internal technology decisions . Monthly active users indicate ongoing activity during a defined month. The share of adults who have ever used ChatGPT is a survey-based measure of cumulative experience, including people who may have tried it once and people who use it regularly. They answer different questions about the same technology. The two ChatGPT metrics measure different behavior The 67.7 million figure is an estimate , not a direct count published by ChatGPT. It comes from eMarketer's market forecasting data, available through its US ChatGPT users and penetration forecasts . Such estimates are useful for sizing current engagement, but the supplied research notes that the detailed methodology is typically available only to subscribers. Pew's 34% result has a different foundation. In its June 25, 2025 release, Pew surveyed US adults and asked whether they had ever used ChatGPT. The increase from 18% in July 2023 to 34% in June 2025 shows that reported familiarity expanded substantially over that period. It does not show how often respondents used the tool, whether they used it for work, or whether they continue to use it. Measure Reported figure What it indicates Key limitation US monthly active users Estimated 67.7 million Current ChatGPT activity in the United States during a month eMarketer market estimate, rather than direct platform analytics US adults who have ever used ChatGPT 34% in June 2025 Self-reported lifetime experience with ChatGPT Does not measure frequency, recency, or work

With football season just hours away, Google has turned its AI into a fantasy football coach.

I built a 659-component React library with a Ticket-Stub soul (MIT, zero deps) Most component libraries look the same. I wanted one that feels like something — a well-printed admission ticket: warm cream paper, ink lines, a red rubber stamp. That's bigbullui , now at v1.2.0. What it is: 659 copy-paste React 19 + Tailwind v4 components, 63 page blocks, 1023 hand-drawn stroke icons. Zero runtime dependencies — every file imports only react and a 7-line cn helper. MIT, no attribution needed. Three ways to use it: npx bigbullui add ticket-card # steal one component npm i bigbullui # take the package npx shadcn@latest add https://ui.bigbullapp.com/r/button.json # shadcn CLI works too Blocks work the same way ( .../r/admin-overview.json bundles the block plus its components), and npx create-bigbull-app my-app scaffolds a starter. For the AI-coding era: it ships an MCP server so Cursor/Claude Code can list components, fetch source, and read theme tokens directly — no backend, it runs locally: claude mcp add bigbullui -- npx -y bigbullui mcp Try before you install: /create — pick components, icons, and a theme, get one install command. Or browse all 659 . Built in the open, feedback welcome — what's missing for your stack?

OpenAI's announcement Tuesday that it has solved one of mathematics' legendary Millennium Prize problems should have been a moment of triumph. The result is both an undeniable achievement and a striking demonstration of just how rapidly AI is transforming mathematics. But before it was even formally announced, the breakthrough had been complicated by the unusual […]

At Wednesday's iPhone Duo launch event, Apple announced a handful of new Siri AI Audio Intelligence features, including Siri Recap, Live Rewind, Sound Recognition, and Music Recognition. Alongside its announcement, Apple released a document laying out how it plans to balance AI "ambient listening" and users' privacy. It says the raw audio from the new […]

Apple says its new watches won’t save raw audio, but features that can transcribe recent speech and summarize ambient conversations raise new questions about consent, privacy, and how people behave when they know they could always be recorded.

US urges AI firms to ID, then secretly switch, Chinese users to less-capable models.

The main event was the tech giant's highly anticipated first foldable phone, the iPhone Duo.