标签：#ci

On the benchmark that measures automating actual business processes, Claude Fable 5.1 scored 31.4% — up from 17.1% for Claude Fable 5, released three months earlier. Anthropic calls that benchmark AutomationBench. A near-doubling in one release cycle is the number worth stopping on, because most of the automation work I build for clients lives or dies on exactly that capability: can the model finish a multi-step job without a human stepping in. Here is a clear-eyed read of what Claude Fable 5.1 changes for business automation, what it actually costs once you account for how it behaves, and when Fable 5 or Opus 5 is still the right call. TL;DR Anthropic released Claude Fable 5.1 and Mythos 5.1 on 1 September 2026. Fable 5.1 is generally available; Mythos 5.1 is restricted to vetted cybersecurity and life-sciences organisations. Anthropic reports Fable 5.1 scores 31.4% on AutomationBench (business-workflow automation), up from 17.1% for Fable 5, with large gains on agentic coding and research benchmarks too. Base API pricing is unchanged at $10 / $50 per million input/output tokens. The one cut is cache reads, down 75% to $0.25 per million. Independent analysis by Stork.AI reports Fable 5.1 emits about 1.7x more output tokens per task, so it is cheaper only when cached context dominates your spend — long-running agents on a stable codebase or knowledge base. For varied one-off prompts, Opus 5 or Sonnet 5 is better economics. What is Claude Fable 5.1? Claude Fable 5.1 is Anthropic's flagship model for coding and knowledge work, released on 1 September 2026 as an incremental upgrade to Claude Fable 5. The same underlying model ships in two safeguard configurations: Fable 5.1 — generally available. API id claude-fable-5-1 , on the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, Microsoft Azure AI Foundry, Claude Code and Claude Enterprise. Mythos 5.1 — restricted. Lighter safeguards for vetted organisations via Anthropic's Cyber Verification and Life Sciences Veri

The discovery adds to the planet’s history of strange atmospheric phenomena that scientists don’t full understand.

Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part…

I've been a die-hard Computer Science fan for as long as I can remember. Right after my 10th standard, I picked up C — that was four years ago. Around the same time, GitHub pulled me in before I even understood what was happening there. I couldn't parse a single line of what people were building, but I could tell something big was going on. That curiosity eventually pulled me into web development, and from there, into almost every corner of tech over the next few years — AI included. Diploma: The Real Lessons Weren't in the Syllabus I just finished a 3-year Diploma in Computer Engineering. Looking back, the biggest lessons weren't in the coursework. They were in hallway conversations — friends and teachers talking about where technology and the market are headed, instead of the usual teenage small talk. Watching how an organization actually runs, what really happens day to day — that taught me more than most subjects did. A Habit I Used to See as a Flaw Here's a pattern about how I work: everything I start, I start from zero — and I don't always go deep. I finish with the basics, then move on. For a long time I saw that as a bad habit. Three years and almost every major technology later, I've changed my mind — it was the fastest way to find out that "a little bit of everything" isn't who I am. What I actually need is to dig into a system until I find the reason it works. Until I do, I can't let it go. Where That Instinct Pointed Me: Cybersecurity That same need to dig eventually pointed me toward something equal parts fun and dangerous — cybersecurity. I'm about three months into this path now, and I'm moving slowly. Not because it's too hard, but because I won't move to the next topic until every dot is connected. Loose ends don't let me sleep. What I've Learned So Far This is still the floor, not the ceiling, but it's real and hands-on: Web authentication attacks — 2FA bypass, broken password-reset logic, username enumeration through timing differences, account lo

A San Francisco “Hot List” is trying to buck stereotypes about dating in tech circles amid wider debates about the value of being perceived as attractive.

Every charting tool eventually gets the same feature request: "show me other times this stock looked like this." It sounds like a lookup. It is not. The retrieval is the easy half. The hard half is that a correct implementation can still produce results that are quietly meaningless, and nothing in the code will tell you. Here is the method, and the failure modes worth knowing before you ship it. No claims about predictive power anywhere in this piece — the last section explains why that is a deliberate choice, not a hedge. The naive version, and why it fails immediately The obvious first attempt: take the last 30 days of closing prices as a query vector, slide it across history, compute Euclidean distance, return the closest matches. import numpy as np def naive_search ( history , query , k = 5 ): m = len ( query ) windows = np . lib . stride_tricks . sliding_window_view ( history , m ) dists = np . linalg . norm ( windows - query , axis = 1 ) idx = np . argsort ( dists )[: k ] return idx , dists [ idx ] Run this and you get garbage — but instructively specific garbage. Every match comes from whatever period had a similar price level . Query a stock trading at $180 and you get back the other times it traded near $180. The shape is irrelevant to the metric; the offset dominates it. Scale is the same problem in a different coat. A stock that moved 2% over the window and one that moved 40% can trace an identical shape, and raw distance calls them unrelated. Normalize per window, not globally The fix is to z-normalize each window independently: def znorm ( x , axis =- 1 , eps = 1e-8 ): mu = x . mean ( axis = axis , keepdims = True ) sd = x . std ( axis = axis , keepdims = True ) return ( x - mu ) / ( sd + eps ) Per-window is the load-bearing part. Normalizing the whole series once preserves the relative offsets you were trying to remove. Each candidate window has to be centered and scaled on its own terms before it's compared. There's a satisfying identity waiting here.

Have you ever wondered how your smartwatch actually knows you're stressed? Most of us treat the "Stress Score" on our wrists as a source of truth, but the logic remains hidden behind proprietary algorithms. Today, we are pulling back the curtain. We are going beyond basic heart rate tracking to perform PPG signal processing and HRV frequency domain analysis using Python. By the end of this guide, you’ll know how to ingest raw data via Bluetooth Low Energy (BLE) , apply digital filters with SciPy , and calculate the elusive LF/HF ratio to determine autonomic nervous system balance. If you are interested in advanced biometric algorithms or Python signal analysis , you’re in the right place. 🚀 The Architecture: From Photons to Stress Metrics Unlike standard heart rate (BPM), which just counts peaks, Stress Scores rely on Heart Rate Variability (HRV) —the millisecond-level variations between heartbeats. We'll be moving from raw light intensity data to a frequency-based stress index. graph TD A[Wearable Sensor / PPG] -->|Raw BLE Stream| B[Data Acquisition - Bleak] B --> C[Preprocessing - Bandpass Filter] C --> D[Peak Detection - Find R-R Intervals] D --> E[Cubic Spline Interpolation] E --> F[Fast Fourier Transform - FFT] F --> G[LF/HF Ratio Calculation] G --> H[Final Stress Score] 🛠 Prerequisites To follow this advanced tutorial, you’ll need: Hardware : A pulse oximeter or wearable that exposes raw PPG via BLE (e.g., Polar OH1, MAX30102 with an ESP32). Stack : NumPy & SciPy : For heavy-duty math and signal processing. Bleak : For cross-platform Bluetooth Low Energy communication. Matplotlib : To visualize the pulse waves. Step 1: Capturing the Raw PPG Stream (BLE) Photoplethysmography (PPG) works by shining green or red light into the skin and measuring the light absorption. First, let's grab that raw stream. import asyncio from bleak import BleakClient # UUID for the Raw PPG Characteristic (Device specific) PPG_CHAR_UUID = " 00002a37-0000-1000-8000-00805f9b34fb " def no

Most "best CI/CD tools" lists are twenty logos in a table, ranked by nothing in particular, with the author's product at the top. This is not that. It is a map of the categories, what each one exists to solve, and how to tell whether you need it yet. I work at Latchkey, so I will say plainly where we sit: we are one option inside one of the six categories below, and I will tell you when we are the wrong answer. Read the rest as a map, not a pitch. A note on what is missing here: I have not invented benchmark numbers or quoted prices for tools I do not operate. Vendor pricing changes often enough that any figure I write today is wrong by the time you read it. Where a number matters, go to the vendor's own pricing page. The mistake most teams make Teams usually shop for CI/CD tools by asking "which one is best." That question has no answer, because the tools are not competing with each other. They are stacked on top of each other. A team that adopts a build accelerator to fix a slow pipeline, when the actual problem is that half their failures are flaky, has bought a faster way to fail. A team that adds pipeline observability before they have enough pipeline to observe has bought a dashboard nobody opens. The useful question is narrower: which layer is currently your constraint? Answer that, and the tool choice inside the layer is usually obvious. Here is the whole landscape in one view. Layer What it solves When it becomes your bottleneck CI platform Running the pipeline at all Never; this is where everyone starts Runners and compute Where jobs run, and how fast they start Queue time or runner cost is visible Build acceleration Doing less work per run Full rebuilds dominate your wall clock Supply chain security What the pipeline is allowed to reach You ship to production or touch customer data Observability and cost Where time and money actually go You cannot answer why last week was slow Artifacts and registries Storing what the pipeline produces You publish images

Two things are true about software delivery in 2026, and they are pulling in opposite directions. The first: AI is now writing a large share of the code that reaches your pipeline. CloudBees' 2026 State of Code Abundance Report found that AI generates or assists in writing 61% of the average enterprise codebase, and that 81% of enterprise leaders report an increase in production issues tied to AI-generated code. The same report names a confidence gap worth sitting with: 92% of leaders say they are confident in the production readiness of that code, even as the failures climb ( CloudBees, 2026 ). The second: the place best positioned to catch those failures, the CI/CD pipeline, is where teams trust AI the least. JetBrains' TeamCity team reported that 73% of organizations do not use AI in their CI/CD pipelines at all, and 78.2% do not delegate tasks to AI in CI/CD workflows, even though general AI usage in development work exceeds 90%. When asked why, 60% cited unclear use cases or value, 36% cited a lack of trust in AI-generated results, and 33% cited data privacy concerns ( JetBrains TeamCity, 2026 ). That is the trust gap. More machine-written code is arriving, more of it is breaking in production, and the pipeline that should be the safety net is the one room teams will not let automation into. This piece is about why that hesitation is rational, and what automation has to look like to deserve a different answer. Why the pipeline is different The JetBrains analysis put its finger on the reason cleanly: development workflows tolerate experimentation because feedback is immediate and cheap. CI/CD is the opposite. It demands consistent, reproducible signals, and the cost of an error is high. A coding assistant that guesses wrong wastes a few seconds of your time. A pipeline that guesses wrong can hide a real defect, ship it, or erode the one thing a pipeline exists to provide: a trustworthy answer to the question "is this build good?" So the bar for automation in CI/

The first half of 2026 reset two things at once for engineering teams: what continuous integration costs, and what it takes to keep delivery stable while AI pushes more change through your pipelines than ever. Those two stories are connected, and the connection is the part worth your time. The pricing reset On January 1, 2026, GitHub reduced prices for GitHub-hosted runners by up to 39%, with the size of the cut depending on the machine type ( GitHub Changelog ). Standard hosted-runner usage on public repositories stays free, as it was before. The DevOps publication SamExpert documented the specific per-minute moves. A Linux 2-core runner dropped about 25% (from $0.008 to $0.006 per minute). A Windows 2-core runner dropped about 38% (from $0.016 to $0.010). A Linux 64-core arm64 runner dropped about 39% (from $0.160 to $0.098) ( SamExpert ). If your CI runs mostly on hosted runners, that is real money back, and it is worth recalculating your monthly estimate against the new rates rather than assuming last year's numbers still hold. The same December 2025 announcement carried a more controversial proposal: a $0.002 per-minute charge for self-hosted runner usage in private repositories, scheduled to start March 1, 2026 ( DevClass ). GitHub framed it as ending a cross-subsidy, where revenue from hosted runners was effectively underwriting the cost of operating Actions for everyone, and said the large majority of customers would see no change to their bill. The reaction from developers who run CI on their own hardware was sharp, with some publishing the monthly figures they expected to owe for compute they already pay to operate themselves. Within about a week, GitHub posted that it was postponing the self-hosted billing change to re-evaluate its approach ( SamExpert ). Postponed, it is worth being precise here, is not the same as withdrawn. There is no new date and no guarantee the charge returns in its original form, but there is also no statement that it is gone for

The first half of 2026 rearranged three things at once for teams that live in GitHub Actions: what CI costs, how it is secured, and how much of it a machine can now do on its own. None of these landed cleanly. Prices went down for most people while a new platform charge quietly went up. A self-hosted runner fee was announced, met a wall of objections, and was pulled back within a week. And a security roadmap arrived that will change how workflows pin dependencies and scope secrets over the next two to three quarters. Here is a grounded read of what happened, with sources, and an honest account of where Latchkey fits. Hosted runners got cheaper, and a new platform charge arrived On January 1, 2026, GitHub reduced GitHub-hosted runner prices by up to 39%, with the size of the cut depending on the machine type (larger runners saw the larger relative reductions), per GitHub's own changelog ( github.blog ). In concrete terms, community reporting put the Linux 2-core rate moving from $0.008 to $0.006 per minute and the Windows 2-core rate from $0.016 to $0.010 per minute ( samexpert.com ). Alongside the cuts, GitHub introduced a $0.002 per-minute Actions cloud platform charge that applies to all Actions workflows. For GitHub-hosted runners, that charge is already bundled into the reduced meter price, so it is not a separate line item there ( github.com ). Two things stayed the same and are worth repeating, because they get lost in the noise: standard runner usage on public repositories remains free, and GitHub Enterprise Server pricing is unaffected ( github.com ). GitHub framed the net effect as small for most accounts: it stated that 96% of customers would see no change to their bill, and that of the 4% affected, 85% would see costs decrease while the remaining 15% faced a median increase of roughly $13 ( github.com ). That is a reassuring headline. It is also a reminder that the bill depends entirely on your own mix of runner sizes and minutes, which is exactly the thi

Two forces are pulling on delivery pipelines this year. Code is arriving faster than ever, and the cost of running the pipelines that ship it has been unusually unsettled. Let us look at both, honestly, and then talk about where reliability work fits. Pricing was a moving target, and it still is On December 16, 2025, GitHub announced a simpler Actions pricing model that included a new $0.002 per minute "cloud platform charge." The plan was for that charge to reach self-hosted runner usage in private repositories on March 1, 2026 ( GitHub Changelog ). The reaction was strong enough that GitHub reversed the self-hosted portion within days. As GitHub put it, they "missed the mark with this change by not including more of you in our planning," and postponed the self-hosted charge to re-evaluate the approach ( GitHub Changelog ). Postponed is not cancelled, so if you run self-hosted runners in private repos, this is worth watching rather than filing away. GitHub's own framing was that the change would touch a small slice of accounts: it reported that 96% of customers would see no change to their bill, and that of the 4% affected, most would actually see their Actions bill decrease ( GitHub Changelog ). Even so, the principle of paying a per-minute fee for software running on hardware you already own was the sticking point for many teams, and the reversal followed quickly. The other half of the announcement did take effect. On January 1, 2026, GitHub reduced the price of GitHub-hosted runners by up to 39%, depending on the machine type, while leaving free minute quotas unchanged ( GitHub Changelog ). GitHub pointed teams to its runner pricing docs and calculator for the exact per-machine rates rather than publishing a single headline number ( GitHub Changelog ). That "up to" is doing real work in the sentence: the reduction depends on which machines you actually use, so the only way to know your number is to look at your own usage mix. The practical takeaway: the ground u

Gene-edited pig kidneys could offer a lifeline to patients stuck waiting for a human donor.

CDC staff had already accepted the measles death reports when RFK Jr. meddled.

ChatGPT, Claude, and Grok all suffered outages at nearly the exact same time for reasons that remain murky.

Stryker says its SportSuite Vision software for the headset received De Novo authorization from the FDA in July.

Your CircleCI pipeline is green. Every job passes. And yet your app is running against a dependency version that hasn't shipped in a month — nobody committed it, nobody bumped it, it just quietly showed up in production. If you've chased a bug like this, the culprit is almost never your code. It's your cache key. This is a five-minute read and a fifteen-minute fix. Quick Win Friday, deployed to your .circleci/config.yml . The failure mode CircleCI's dependency caching works on a simple contract: you compute a key from something that changes when your dependencies change (usually a lockfile checksum), and you save/restore a cache tied to that key. The contract breaks in three specific, extremely common ways: You checksum the wrong file. {{ checksum "package.json" }} looks reasonable until someone bumps a transitive dependency via package-lock.json without touching package.json . The checksum doesn't move. CircleCI happily hands back last week's node_modules . restore_keys does prefix matching, and people think it does exact matching. CircleCI tries your primary key first, then falls through restore_keys in order, and the first one is a prefix match against existing cache entries — not "give me the newest exact match." If your restore_keys list is too coarse (e.g. just v1-deps- ), you can restore a cache built from a completely different branch, with a completely different lockfile, and the job won't fail. It'll just quietly install nothing (cache hit, npm ci sees the modules are "there") or run against the wrong versions. There's no version escape hatch. When you inevitably need to force everyone's cache to invalidate — a corrupted cache entry, a package manager migration, a lockfile format change — there's no cheap way to do it, because the key format was never designed with a manual buster in mind. Each of these fails silently. No red X. No error in the logs. Just a build that ran with stale state, and a bug report three days later that nobody can reproduce locally

Insect-inspired "sparse coding" does fast learning, avoids catastrophic forgetting.

OpenAI leaders think the company’s next generation model, which excels at computer use and coding, may mark a major milestone in AI development.

It's also letting people upload multiple photos at once when reacting to a post.