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Microsoft has enabled zone redundancy on the Standard v2 tier of Azure API Management, following its arrival on Premium v2 in December. Standard v2 starts at $700 per month against $2,801 for Premium v2, but carries a 99.95% SLA rather than 99.99%. Zone redundancy can only be configured when creating an instance. By Steef-Jan Wiggers

I could not make myself log time in Jira. Not because it is hard. Because nothing happens afterwards. You type a number into a box, the box says nothing back, and by Thursday the habit is gone again. Every tool I tried fixed this by adding another box. So I built the missing half instead. Feed The Troll gives everyone on a team a pixel-art troll that gains XP from the work they already do in Jira, and turns sprint results into a village the whole project shares. It is on the Atlassian Marketplace now. This post skips the game itself. It is about five problems that turned out to be hard in ways I did not expect, each one a consequence of building the thing on Atlassian Forge, alone. What Forge gives you, and what it takes back Forge runs your code on Atlassian's infrastructure. There is no server of mine anywhere in the picture. That is the line on the listing page, and it was the single fact that shaped every decision underneath it. You get a Node 22 runtime, Forge SQL (TiDB under the hood) for storage, and Custom UI modules that reach the backend through @forge/bridge . You give up a backend you control, a cache you can reach, and outbound HTTP to anything you did not declare. The one that keeps mattering: any way to open the database at three in the morning and fix a single row by hand. The whole app declares six scopes. None of them are write scopes: read:board-scope:jira-software read:issue-details:jira read:jira-work read:jira-user read:sprint:jira-software storage:app That last line is the entire persistence layer. Twenty-one tables live behind it now, but only ten shipped with v1.0: trolls, XP events, daily activity, kudos, quests, inventory, team quests, villages, raids, project settings. Every table added since arrived the only way the platform makes comfortable, as a new migration appended to the list, never an edit to one already deployed. migrationRunner . enqueue ( ' v001_create_trolls ' , CREATE_TROLLS_TABLE ) // ... . enqueue ( ' v012_create_product_m

CERN engineers announced a shift from Red Hat-based distributions to Debian for its accelerator control systems. This decision stems from Red Hat's tightening compiler mandates, which threatened legacy hardware. The transition, focused on 2,200 specialized control machines, is set for completion in late 2026, while CERN's other systems will remain with Red Hat and AlmaLinux. By Olimpiu Pop

Half a day lost is the honest cost of treating an AI model like a document. I discovered that the hard way: a CAPA opened for a data-provenance gap rolled forward into missing documentation, which then exposed weaknesses in change control and supplier traceability. This is what happened, what we changed, and the small automation that stopped the loop from repeating. The trigger: a CAPA that looked simple and wasn't An engineer flagged a discrepancy between on-device inference behaviour and the validation test bench. The CAPA looked routine: reproduce, find root cause, correct datasets or model weights. Quickly it turned into: We couldn't identify which training dataset produced the deployed model (no manifest, only folder names). Preprocessing steps changed between runs (different label encodings, a silent resampling step). Model binaries were overwritten in a shared location without an immutable model registry entry. Change control only referenced a release ticket number — not the dataset or container image digest. What began as a data-provenance finding became a documentation finding, then a change-control finding. Auditors would call this a traceability gap. The EU AI Act (and notified bodies increasingly expect traceability for high‑risk AI components) means you must show how a model version ties to the data, the training pipeline, the verification evidence, and the approval record. We didn't have that linkage. By midday my filter coffee was cold and I had a long list of evidence to assemble. Why CMOs see this differently As a CMO handling components and supplier networks, our "models" are often supplier-provided (analytics, inspection classifiers, OCR of COAs), or built from datasets stitched from multiple vendors. The usual eQMS workflows assume a device maker controls the full pipeline. They rarely fit a supplier-heavy reality where: Sub-tier suppliers supply datasets or models. Incoming inspection depends on vendor-provided models for automated checks. Suppl

Most countries tax immovable property, and most of them argue about it badly. The argument usually skips the part that decides the answer: before any map is coloured, someone has to choose what is added up, what it is divided by, over which territory it is aggregated, and which cases are left out . Change any of those and the map can change while the underlying data stay identical. This post works through that problem with Chilean data, because Chile happens to publish the pieces needed to do it honestly: a national cadastre of every taxable property, and an official index that ranks small civic territories by socio-territorial vulnerability. The mechanics, though, are not Chilean. Any jurisdiction that assesses property for tax and then maps the result against a deprivation measure faces exactly the same four choices. The question fits in one small fraction: territorial indicator the unit you compare it against the total you want to describe ​ Adding up the assessed value inside a territory answers how much administrative value was allocated there. Dividing that same total by households, by residents or by square metres answers different questions. None of them is «the correct one» by nature; the error appears when one is presented under another's name. The arithmetic is usually innocent. The narrative is not always. Reading contract I cross two Chilean administrative registers: the real-estate cadastre of the Servicio de Impuestos Internos (SII) —Chile's tax authority, roughly the counterpart of the IRS or HMRC— and the Índice Global de Vulnerabilidad Socioterritorial (IGVUST) , a socio-territorial vulnerability index published by the Ministry of Social Development and Family. The unit of analysis is the neighbourhood unit , not the parcel, the household or the person. A word on that unit, because it has no clean equivalent elsewhere and it drives half of what follows. A Chilean unidad vecinal (UV) is a civic territory drawn for neighbourhood organisation and loca

Status of the demo. The viewer was regenerated on August 29, 2026 from a sanitized public projection (with the non-public bits stripped out 😀): the artifact contains no mail bodies, attachments, addresses, absolute paths, tokens, credentials, or microdata. Preamble: remembering is not enough In the second part I went after a bounded problem: getting penta-agent 's memory to retrieve evidence and to recognize when it had found none. The question in this third part is more practical, and it comes out of the system having been in use for a while: what happens to a memory as it grows and turns blurry, or even contradictory? An index can pile up fragments without any trouble, and there are plenty of tools that already do that well. A more useful memory, in my judgment, has to carry provenance, currency, permissions, contradictions, and deletion criteria. It also has to tell finding a source apart from using it correctly. Recent literature insists on separating RAG — retrieval-augmented generation — context management, and agent memory, because they do different jobs and call for different evaluations 1 . What follows has three movements: what changed since part II; which experiments survived a more serious evaluation; and how to show a memory without passing it off as a mind. Spoonful 1: from retrieving fragments to governing evidence In part II the problem was retrieving well : finding the relevant context and recognizing when there was not enough evidence. A useful memory does not only retrieve information; it also has to know where it came from, whether it still holds, where it can be used, and what is allowed to be done with it . RAG mostly solves retrieval. The memory layer adds rules for keeping, updating, relating, or discarding evidence. None of those functions amounts, on its own, to identity. To describe provenance I use concepts compatible with PROV-O — entities, activities, and agents — while currency, sensitivity, and permissions need rules of their own 2 .

In the first post I described how I organized my local multi-agent setup, penta-agent : Codex executes, Claude reviews, other agents enter in bounded ways, and the human keeps closure authority. I also argued that operational memory should not depend on a single conversation or be confused with the vector index. By the time I closed that first post, I already had continuity mechanisms: handoffs, routing rules, append-only logs, experiential memory in JSONL/YAML, a rebuildable vector collection, and the recall-context skill. My problem was not absolute amnesia. It was that I still could not prove what the system retrieved, when it confused a coincidence with evidence, and when it should admit that it did not have an answer. This second part, then, is not about inventing memory from scratch. It is about turning still-fragile operational continuity into a traceable, testable, and rebuildable mechanism. The idea of an external working memory is not new. It echoes Bush's old ambition of augmenting recall through a personal archive and the extended-mind intuition that notes and tools can become part of cognition. 1 2 My claim here is narrower: local traces are useful only if I can retrieve them with provenance and audit how they were used. Spoonful 1: the problem was not storing, but retrieving well Storing information is easy. The difficult part, I think, is retrieving the right piece when there are successive decisions, similar names, contradictory versions, and explanations spread across several files. To organize that "memory" in my own setup, I separated its operational layers: Table 1 - System memory layers Layer Question it answers Effective implementation Canonical record What happened, and what was decided? memory/experience-events.jsonl , memory/experience-lessons.yaml , memory/interaction-metrics.jsonl , and curated context events. Retrieval index Where is the relevant evidence? Qdrant with penta_context_v2 for curated context and penta_experience_v1 for operat

Authors say publishers seem to be claiming more than their fair share of settlement payments.

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An AI agent that has no job description will invent one. That is the whole reason agents drift, and it is the reason most of the agents I have seen deployed inside Indian businesses are quietly switched off within a few months of going live. Nobody would hire a person, point them at the office, and say "handle things". Yet that is exactly how most owners deploy an agent. They connect it to WhatsApp or email or the accounts folder, give it a paragraph of instructions, and let it run. Then they are surprised when it starts answering questions it was never meant to answer, promising delivery dates it cannot know, or filing something that a human should have looked at first. The fix is not a better model. It is the same discipline you already use for people: defined duties, an escalation path, a probation period with a review date, and one named person who is accountable for it. What drift actually looks like Drift is not a dramatic failure. It is a slow widening of scope that nobody approved. A distributor in the FMCG trade sets up an agent to acknowledge incoming orders on WhatsApp and log them into a sheet. Week one, it does that. Week three, a retailer asks "when will my stock reach?" and the agent, being helpful, answers with a guess. Week five, a retailer asks for a discount, and the agent, having seen discounts mentioned in earlier messages, offers one. None of this was in the brief. All of it followed naturally from "be helpful to customers", which is what the owner wrote because they did not know what else to write. By the time the owner notices, the agent has made commitments in writing to twenty retailers, and the sales team is cleaning up after it. The agent did not malfunction. It did what an unsupervised new employee does: it filled the vacuum with its own judgement. The mistake was upstream, at the moment of deployment. The job description A job description for an agent is not a prompt. It is a one-page document the owner can read and sign off, written in

Modern price monitoring systems need to do more than tell you that a price changed. A single abnormal listing, a scraped error, or a temporary outlier can make a traditional threshold-based detector fire an alert when nothing meaningful happened. In this project, I built a lightweight real-time price anomaly detector in Python that combines: A rolling median baseline Median Absolute Deviation (MAD) Robust Z-scores Short-term percentage returns Trend confirmation Alert cooldowns The goal is simple: detect meaningful price movements without overreacting to noisy observations. Note: This project monitors retail prices from Google Shopping results through SerpApi. It is a retail-price monitoring example, not a financial exchange-data feed. What we're building The pipeline looks like this: ┌──────────────────────┐ │ SerpApi / Shopping │ └──────────┬───────────┘ │ ▼ ┌──────────────────┐ │ Price Extraction │ │ + Validation │ └────────┬─────────┘ │ ▼ ┌────────────────────┐ │ Rolling Price │ │ History │ └────────┬───────────┘ │ ┌───────────┼───────────┐ ▼ ▼ ▼ Median MAD Return % │ │ │ └───────────┼───────────┘ ▼ Robust Z-score │ ▼ Trend Confirmation │ ▼ Signal Engine │ ▼ Alert Cooldown The implementation is intentionally small and interpretable. The complete engine is built around a single PriceAlertEngine class and a compact AnomalyResult data structure. Why not just use standard deviation? A common first implementation is: price > mean + 3 * standard_deviation The problem is that standard deviation is sensitive to extreme observations. Suppose your historical prices are: 990, 995, 999, 1001, 1005 Then one bad observation such as: 1500 can distort the mean and standard deviation. That can move your detection boundary away from the actual market behavior you are trying to model. For a noisy retail environment, a more robust baseline is useful. That's where median and Median Absolute Deviation come in. 1. Building a rolling median baseline Instead of storing an unlimited stre

"We achieved within a few years what had taken the European space industry decades before."

Published 2026-09-06 This describes NIST IR 8547 ipd — the initial public draft of November 2024 — as it stood on the date above. Its dates are proposed. If a final IR 8547 has published since you are reading this, check it: the numbers below may have moved, and the article's central caveat may no longer apply. If you have read anything about post-quantum migration in the last year, you have read that RSA is deprecated in 2030. It is one of those facts that has been repeated into the shape of a rule. It is half true, and the half that is false is the half people plan around. Here is the actual table, from NIST IR 8547, Transition to Post-Quantum Cryptography Standards : algorithm parameters transition RSA, ECDSA 112 bits of security strength Deprecated after 2030 Disallowed after 2035 RSA, ECDSA, EdDSA ≥ 128 bits of security strength Disallowed after 2035 Read the second row again. At 128 bits and above there is no 2030 row at all . RSA-3072 is not deprecated in 2030. Neither is P-256. The 2030 date applies to 112-bit strength — RSA-2048, P-224, 2048-bit finite-field Diffie-Hellman — and to nothing else. The key-establishment table (Table 4, covering finite-field DH/MQV, elliptic curve DH/MQV and RSA) has exactly the same shape. Same split, same dates. So an inventory that reports "47 uses of RSA, all deprecated in 2030" is reporting something the document does not say. Some of those uses are on a 2030 clock and some are on a 2035 clock, and which is which depends on a field most tooling does not look at. Deprecated is not disallowed The second thing worth getting right is what the two words mean, because they are not synonyms and NIST defines both: deprecated — "The algorithm and key length may be used, but the user must accept some security risk." disallowed — "The algorithm or key length is no longer allowed for applying cryptographic protection." Deprecated is a risk acceptance. You may continue, with your eyes open and presumably a note in a register somewhere.

If you have ever tried to programmatically get the current RBA cash rate, you know the drill. You open the RBA F1 statistical table, download f01hist.xls, write a pandas.read_excel call, fight with the multi-row header (Series ID on row 11, units on row 6), filter, sort, take the last row. That is 30 lines of code to get a single number that changes 11 times a year. One line (MIT, no key) from rba_mcp import client print ( client . latest ( " F1_1 " , series = " cash_rate_target " ). records [ - 1 ]. value ) # live AU.CASHRATE as of 2026-09-03: 4.35 pip install rba-mcp No key. MIT-licensed. Attribution and source URL come back with the number. Hosted gateway Use GET /v1/series/AU.CASHRATE/latest on api.ausdata.io with a free key from ausdata.io (500 calls/mo). Live 2026-09-06: cash 4.35 percent, trimmed-mean CPI 3.6 percent (2026-Q2), real rate 0.75 percent. Why the hosted path exists The RBA publishes the nominal cash rate. The ABS publishes inflation. Neither publishes the real cash rate (nominal minus trimmed-mean CPI). Use /v1/real-rate-regime for that join. Same envelope across nine AU sources: source, source_url, attribution, retrieved_at. MCP Wire ausdata-mcp via npx in Claude Desktop or Cursor. Sisters on PyPI run fully local with no key. What this is not Suburb-level property prices Live KYC / company-officer lookup 5-minute wholesale electricity bid stacks AU macro public data, one envelope, citations done for you. R users readrba by Matt Cowgill is the R equivalent. This is the Python / JS / agent path. Links Canonical: https://ausdata.io/blog/rba-cash-rate-python-api/ Free key: https://ausdata.io Series: https://ausdata.io/series/AU.CASHRATE PyPI: rba-mcp

The choice is usually made by whoever draws the first diagram, and then everybody lives with it for years. It is worth five minutes of thought, and the deciding question is not which tool is best but who will edit this thing next. Short answer. A visual editor such as draw.io for diagrams that non-engineers maintain. Mermaid or PlantUML when the diagram belongs with the code and should be reviewed like code. An attached SVG when the picture comes from a design tool and you need it to look exactly right. A screenshot when the diagram will genuinely never change again. The four options A diagramming app inside Confluence draw.io is the common choice, and it is free for small teams. The diagram is created and edited inside the page, links on shapes work, and anyone who can use a mouse can maintain it. This is the default answer for architecture maps, process flows and floor plans that live in the documentation and get corrected by whoever notices the mistake. The cost is lock-in of a mild kind: the diagram lives in the app's format, and moving to something else later means exporting and redrawing. Mermaid or PlantUML: diagrams as text Here the diagram is source code — a few lines describing nodes and arrows, rendered into a picture. The appeal is real: text goes into version control, diffs are readable, and a diagram can be generated by a script from the system it describes. Two things to know before choosing this. Confluence Cloud does not render Mermaid natively, so you need an app for it, and several of them exist including free ones. And the editing audience narrows sharply: a technical writer will not touch a diagram that has to be edited as syntax, so the diagram becomes the property of the engineers, whether you intended that or not. An SVG made somewhere else The diagram comes from Figma, Illustrator, Inkscape, Visio or an architecture tool, and lands on the page as an attachment. It looks exactly as designed, which is why people do it. The catch is documented

Two more news organizations are suing OpenAI and Microsoft over the supposed use of their journalism to train AI.

So now that you have a basic understanding of how Terraform works , before you start running any terraform command against a real AWS account, two things need to happen: you need an identity Terraform can authenticate as, and you need a mental picture of what you're about to create, so the plan output in Part 4 isn't just a list of unfamiliar resource names. Never Use Your AWS Root User! The root user (the email/password you signed up to AWS with) can do anything , including closing the account. It should basically never be used day-to-day. Instead, create a dedicated IAM user just for this project. In real life you would create a dedicated IAM user for your CI/CD pipeline to automate deployments: AWS Console → IAM → Users → Create user (e.g. terraform-voting-app ). Do not enable AWS Console access, this user only needs programmatic access, i.e. an API key pair. The AWS managed policy AdministratorAccess is the path of least friction, and is what you should use for the IAM user to test things out. but in real life you would go with least privilege approach, learn more about it in AWS EKS IAM policy examples . On the user's Security credentials tab → Create access key → choose "Command Line Interface (CLI)". You'll get an Access Key ID and a Secret Access Key , store them somewhere safe, we will be needing them later. Give Terraform those Credentials The rule: credentials never go inside a .tf file, and never inside terraform.tfvars . So in your local machine or CI/CD pipeline you need to export AWS_ACCESS_KEY_ID , AWS_SECRET_ACCESS_KEY , and AWS_REGION as environment variables in the shell. Then you won't be needing aws configure or aws login step anywhere, the aws provider has no access_key / secret_key arguments of its own, so it falls back to the AWS SDK's standard credential chain, which checks these exact environment variables first. The AWS CLI and, later kubectl read the same variables. In my local machine I do export an env variable files using a shell scrip

Hello, fellow version-bumping enthusiasts, sleep-deprived Rustaceans, and accidental software...

A federal court decided that "Twitter" was off-limits, but tweet and the bird logo were fair game.