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You're just a few taps away from getting your Android phone out of safe mode and back to normal.

JioHotstar will only have entertainment content when it launches in the UK, Canada, and Singapore.

Originally published on indiecore.net . I moved this site off a hosted blogging platform onto Cloudflare, with GitHub Actions doing the building and Cloudflare doing the serving. It costs nothing, deploys in about two and a half minutes, and refuses to publish anything that fails its checks. Getting there took longer than it should have. Here is the setup, and the five things that tripped me up — none of which are obvious from the documentation. The shape of it git push └─ GitHub Actions ├─ build generate the site ├─ verify dead links, missing images, bad metadata, broken redirects ├─ Lighthouse fail if performance/accessibility/SEO drop below budget └─ deploy upload to Cloudflare The important part is that deploy depends on the checks . A broken build never reaches the internet. Pull requests get a preview URL; merges to main go live. The triggers and permissions that make that safe: ci-cd.yml — triggers and permissions name : CI/CD # Build and verify every change; deploy previews for PRs and production from main. # Deploy jobs depend on the quality gates, so nothing ships unverified. on : push : branches : [ main ] # The SEO watch ledger is machine-written, is never part of dist/, and is # committed daily. Deploying the site again for it would be pure noise — and # would re-trigger the SEO ping through workflow_run every single day. paths-ignore : - ' _source/seo-watch.json' pull_request : branches : [ main ] workflow_dispatch : # Least privilege by default; individual jobs elevate only what they need. permissions : contents : read # Supersede in-flight runs for a branch, but never interrupt a production deploy. concurrency : group : ci-cd-${{ github.ref }} cancel-in-progress : ${{ github.ref != 'refs/heads/main' }} env : # wrangler ships as a pinned devDependency; never phone home from CI WRANGLER_SEND_METRICS : " false" permissions: contents: read at the top means every job starts with the minimum, and only the one that comments on pull requests gets more. The c

Short answer: for a property-management startup, the cheapest one-key router is the one that minimizes cost per correctly classified moderation report on your own replay set while preserving a provider-neutral request, response, and error contract. Raw token rates alone cannot make that choice. Choice Best fit Main catch Measure first Managed multi-provider router Small team optimizing time-to-first-call Another control plane owns the routing boundary Valid classifications per dollar Self-hosted gateway Team that needs policy and telemetry under its control You own upgrades, capacity, and incident response Operator hours plus inference cost Thin in-app adapters Narrow model set and strict contract control Every new capability adds adapter work Change lead time and test burden My default for an early startup app is a managed router behind a tiny internal interface, with request fixtures stored outside the router. Choose the self-hosted runner-up when data-path control or custom routing policy is more important than low configuration overhead. Choose direct adapters when the application genuinely uses only a small, stable slice of each provider API. This is a decision about portability, not a hunt for a permanent lowest price. OpenAI, Claude, and Gemini differ in message shapes, structured-output behavior, usage accounting, and model lifecycle. A shared API key removes credential sprawl; it doesn't erase those differences. How should a startup app compare token cost across one-key routers? Start with the unit of work: one moderation report reaching a human reviewer with a valid label, confidence, rationale, and trace ID. A property manager does not buy tokens for their own sake. They need reports such as harassment , fraud , safety , or noise triaged consistently enough that urgent cases rise and ambiguous cases stay in the human queue. The useful equation is: effective cost = inference charges + router charges + retries + invalid-output handling + operational labor T

Spent the past week wiring up CI and tightening a few decisions on a backend project. Nothing dramatic happened, but a few things stood out enough to write down. CI is worth setting up early, even on a solo project. First run caught a dependency that worked locally but was never actually declared in requirements.txt . Classic "works on my machine" gap. CI doesn't care what your machine has installed, only what the project actually declares, and that mismatch is exactly the kind of thing that's invisible until something forces the comparison. Pinned dependencies drift more easily than people expect. I had a specific package version pinned for a known compatibility issue, and a later, unrelated install silently bumped it past that pin. Caught it by chance during a review, not because anything alerted me. Worth adding an explicit check for that instead of relying on remembering. 404 over 403 for resources that belong to another user is a small choice with real weight. 403 confirms something exists and you're just not allowed to see it. 404 gives nothing away. Costs a bit of clarity for legitimate callers debugging their own mistakes, but that's a fair trade for not leaking what exists in the system. None of this is complicated. All of it is easy to miss quietly, and only shows up if something is actually checking. That's most of what good backend hygiene turns out to be.

While some of the features see Google playing catch-up to Apple, which already offers similar features for iPhone users, others specifically leverage Gemini to provide various improvements.

The company will give select partners early access to its Astra AI model—so they have time to shore up their defenses.

These are the 15 best Amazon Labor Day deals on WIRED-tested, WIRED-recommended products in 2026.

Apple is hosting its iPhone release event next week, which is rumored to feature the first-ever foldable iPhone.

LLM agents increasingly modify their own prompts, tools, middleware, resources, and execution harnesses at runtime. Such self-evolution can improve capability, but a successful mutation may leave persistent effects that cannot be safely reversed in states different from the one in which it was created. We introduce EvoUndo, a framework for representing, synthesizing, diagnosing, and independently verifying recoverability of model-generated self-modifications across counterfactual states. Across 600 unseen one-shot self-evolution tasks, we identify 197 capability-improving mutations that fail recoverability verification. Under the original recovery representation, conventional repair strategies recover 0/197 of these natural failures. Deterministic oracle analysis recovers 48/197 under the original recovery language L0, while the extended recovery calculus increases empirical oracle recovery to 191/197. A protocol-locked 2×2 grounding-by-expressivity intervention then separates two bottlenecks: exact state-address grounding increases successful recovery from 0/48 to 38/48 (79.2%) when the original language is sufficient, while extending the recovery language enables recovery on 142/143 (99.3%) failures in the oracle-defined S1 stratum. On the primary gpt-oss-120b backbone, adding exact-address diagnostics to the richer language reduces recovery to 133/143 (93.0%); a Qwen3.8-27B replication preserves the grounding and expressivity effects but not this negative interaction, indicating that the latter is model-dependent. These results indicate that reliable agent self-evolution requires co-designing verification, state grounding, witness semantics, and recovery-language expressivity rather than relying on iterative prompting alone. Paper: https://arxiv.org/abs/2608.28363 submitted by /u/AccomplishedLeg1508 [link] [留言]

A nonprofit organization called the Fermi Explorer Mission announced today that it intends to launch a spacecraft to our nearest star system by the end of 2029. It’s a hugely ambitious mission—if all goes well, the spacecraft could take up to 80,000 years to arrive at Alpha Centauri, which is 4.4 light-years away. And the…

Hi everyone, 2nd-year PhD candidate here staring down my first A* submission deadline (AAMAS 2027). I could really use some perspective on theory expectations, especially since I think I’ve methodologically painted myself into a corner. The setup My project started with a clean hypothesis: if architecture X is more robust than Y to perturbation A, and B is a strictly harder version of A, then the X > Y ordering should hold under B as well. I isolated three variables I suspected were driving the effect, ran experiments, and… got results that only partially support the hypothesis, with clear boundary conditions. Where I got stuck Trying to explain the “why” mathematically sent me down a theory rabbit hole. I ended up with two bad options: Claims tied to specific training outputs rather than structural/architectural properties, or Weak, hand-wavy speculations that feel like post-hoc rationalizations. I’m pretty sure I fell into HARKing.. I started building theory after seeing the results instead of deriving predictions beforehand. Furthermore, my codebase is built on an undocumented public repo, and I recently found a bunch of hidden parameters set to wrong values for my setting. I’m currently re-running everything, which is why I’m being vague about specifics. My “insights” from the first round are probably garbage. My actual questions For those who’ve reviewed for or published at AAMAS (or similar A* venues): how much formal theory is actually expected for an empirical MARL paper? Is “here’s the phenomenon, here’s the controlled experiments, here’s a plausible but incomplete theoretical sketch” a death sentence? If the theory ends up being training-dependent rather than structural, is that a sign I should pivot to a lower-tier venue, or can strong empirical characterization + limited theory still fly at A*? How do you recover from HARKing mid-project when you’re under pressure to publish in year 3/4 of a 4-year contract? Any advice on how to salvage the timeline or r

Pasting AI code because it runs feels productive and for today it is! I this post I want to explain...

OpenClaw has released OpenClaw 2.0, a major update to the open-source personal AI agent that changes its installation process, browser interface, memory, skills, automations, plugins, security, and collaboration features. By Daniel Dominguez

This article provides a step by step comparison of three Gemma 4 deployments on a single AWS hosted GPU enabled system. A suite of Python MCP tools is built to simplify management of each deployment, and one benchmark harness is shared across all three so that the runtime is the only variable. https://github.com/xbill9/gemma4-dev The whole exercise cost under three dollars, and that is the part worth keeping. Nineteen instances and about four and a half instance-hours bought three serving sweeps, nine timed boots and a handful of A/B restarts. It also bought five wrong claims, each caught by measuring instead of reasoning. On hardware where a run is expensive, the cheapest of those five would have shipped with a caveat attached. What is this project trying to Do? Three rigs in this monorepo serve google/gemma-4-E2B-it on an AWS G5g instance. One runs vLLM, one runs a pure JAX port, one runs PyTorch with transformers. The hardware is identical and only the runtime slot moves, so this should be the cleanest A/B available. For months it was not, because each rig measured itself with its own harness and quoted its own number. Three harnesses computing three statistics is not a comparison. Prerequisites An AWS account with G-family quota in us-east-1 . Each g5g.2xlarge is 8 vCPU, so 16 vCPU of spot quota runs two at once. A subnet, a security group opening TCP 8000, and an instance profile carrying AmazonSSMManagedInstanceCore plus read on the Hugging Face token secret. A Hugging Face token in Secrets Manager. It is fetched at boot into a root-only EnvironmentFile and never appears in user data. boto3 and the standard credential chain. No AWS CLI shell-outs, no inbound SSH rule, and no private key anywhere in the flow. AWS EC2 G5g Instance g5g.2xlarge — 8 vCPU, 16 GiB host Host CPU AWS Graviton2, aarch64 GPU 1x NVIDIA T4G, Turing, SM 7.5 GPU memory 15,360 MiB per nvidia-smi ; AWS lists 16,384 nominal G5g is the only family AWS ships that puts an NVIDIA GPU behind a Gravi

This article provides a step by step comparison of three Gemma 4 deployments on a single AWS hosted GPU enabled system. A suite of Python MCP tools is built to simplify management of each deployment, and one benchmark harness is shared across all three so that the runtime is the only variable. https://github.com/xbill9/gemma4-dev The whole exercise cost under three dollars, and that is the part worth keeping. Nineteen instances and about four and a half instance-hours bought three serving sweeps, nine timed boots and a handful of A/B restarts. It also bought five wrong claims, each caught by measuring instead of reasoning. On hardware where a run is expensive, the cheapest of those five would have shipped with a caveat attached. What is this project trying to Do? Three rigs in this monorepo serve google/gemma-4-E2B-it on an AWS G5g instance. One runs vLLM, one runs a pure JAX port, one runs PyTorch with transformers. The hardware is identical and only the runtime slot moves, so this should be the cleanest A/B available. For months it was not, because each rig measured itself with its own harness and quoted its own number. Three harnesses computing three statistics is not a comparison. Prerequisites An AWS account with G-family quota in us-east-1 . Each g5g.2xlarge is 8 vCPU, so 16 vCPU of spot quota runs two at once. A subnet, a security group opening TCP 8000, and an instance profile carrying AmazonSSMManagedInstanceCore plus read on the Hugging Face token secret. A Hugging Face token in Secrets Manager. It is fetched at boot into a root-only EnvironmentFile and never appears in user data. boto3 and the standard credential chain. No AWS CLI shell-outs, no inbound SSH rule, and no private key anywhere in the flow. AWS EC2 G5g Instance g5g.2xlarge — 8 vCPU, 16 GiB host Host CPU AWS Graviton2, aarch64 GPU 1x NVIDIA T4G, Turing, SM 7.5 GPU memory 15,360 MiB per nvidia-smi ; AWS lists 16,384 nominal G5g is the only family AWS ships that puts an NVIDIA GPU behind a Gravi

Originally published at nlocoding.com 97% of developers using AI code assistants report faster delivery—but only 41% say their teams get more value out of Agile ceremonies. (Source: GitHub, 2026) Just because AI coding tools are everywhere doesn’t mean teams know what to do with them. The pressure is real: 62% of Fortune 500 companies now require at least one AI development workflow in every sprint (Gartner, 2026). Ignore this, and your velocity drops. Embrace it wrong, and you get spaghetti code faster. AI coding tools change Agile team velocity by 2.9x—when integrated right AI coding tools like GitHub Copilot, Amazon CodeWhisperer, and Tabnine can boost story completion rates by 190% (Forrester, 2026). But there’s a catch: poorly managed integration leaves 54% of teams fighting merge conflicts and technical debt. The difference? Structured onboarding. Assign a team member as AI Integration Lead. Define code review gates for all AI-suggested code. You’ll see fewer reverts, more predictable velocity. 73%Teams reporting higher sprint completion rates after structured AI onboarding (Forrester, 2026) 💡 Pro Tip: Treat AI-generated code as a junior developer’s PR—never deploy without an explicit review. Most people get this wrong: AI tools won’t fix broken Agile rituals Standups don’t run themselves. 61% of teams expect AI to automate reporting, but only 22% actually see improved Sprint Retrospectives after adoption (Atlassian, 2026). Real progress comes from integrating AI code suggestions into backlog grooming and Sprint Planning. Have the team review AI-suggested code branches as part of the definition of done. One fintech startup, FinoStack, cut Sprint Planning time from 4 hours to 1.5 hours by pre-labeling tasks with AI-predicted effort. But their biggest win? Product Owners finally spent more time on priorities, less on code reviews. ⚠️ Common Mistake: Letting AI code suggestions bypass Sprint ceremonies. This breeds shadow code and long-term rework. The data shows

Fixing the “D.map is not a function” crash by tightening DB indexes and normalizing the API payload TL;DR: I added missing PostgreSQL indexes in apps/api/src/db/db.ts and forced the /condos/metrics endpoint to always return an array. The change stopped the runtime TypeError: D.map is not a function in the React selector and restored correct KPI calculations. The Problem Our internal “Condo Dashboard” started throwing a JavaScript error in production: TypeError: D.map is not a function at render (src/components/CondoSelector.tsx:45) at D.map(e=>(0,a.jsx)("option",{value:e.id,children:e.name},e.id)) D is the data array used to populate a <select> with condo options. When the page loaded, the dropdown was empty and the whole component crashed. The API call that feeds D ( GET /api/condos/metrics ) was supposed to return an array of objects { id, name } , but under certain conditions it returned null or a single object, breaking the .map call. The root cause turned out to be duplicate rows in the broker_tokens table that caused the query to return a malformed result set. Those duplicates were a side‑effect of missing unique indexes on the broker_tokens and condo_metrics tables. What I Tried First Guarding the Front‑end – I added a quick check in CondoSelector.tsx : const options = Array . isArray ( data ) ? data : []; This silenced the error, but the UI still showed no options because the API kept returning the wrong shape. It was a band‑aid, not a fix. Manual Data Normalization – In the API controller I forced the result to an array: const rows = await db . query ( sql ); return res . json ( Array . isArray ( rows ) ? rows : [ rows ]); This produced duplicate entries and confused downstream calculations. The KPI numbers in the dashboard were still off. Both approaches addressed the symptom but left the database inconsistency untouched, so the bug could re‑appear anytime new data landed. The Implementation 1. Add proper indexes (the real fix) The missing indexes allowed

Starting on September 1, Ternus will lead one of the world's most valuable companies, but if you're not a dedicated Apple enthusiast, you've probably never heard of this man, who has largely remained out of the spotlight until now.

New features are rolling out across the Android ecosystem starting today.