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Meta is developing plans for a cloud infrastructure business, selling access to AI compute power and models. The move would pit it against the big cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure.

Convenience costs you with Plex, but the alternatives require more work.

The acquisition was approved without concessions by the Department of Justice in June.

Available in select markets thanks to a partnership with Gigs, Motorola phone owners have one less hurdle to clear when signing up for a data-only eSIM before traveling abroad.

Contorium is a local-first system that introduces persistent project cognition into AI-assisted development workflows. Instead of treating AI as a tool that operates on code, Contorium treats the project itself as a structured, evolving system. ⸻ 🧠 Problem Modern AI coding workflows suffer from a structural limitation: Even with tools like: Cursor Claude Code MCP-based agents IDE copilots context is still: fragmented session-based non-persistent weakly structured This leads to: repeated explanations, lost reasoning, and architectural drift ⸻ 🧩 Solution: Project Cognitive Runtime (PCR) Contorium introduces a runtime model where project understanding is persistent and structured. ⸻ Core Components ⸻ PIK — Project Intent Kernel PIK defines the system-level intent of a project: primary goal constraints non-goals priority weighting It acts as a stable semantic anchor. ⸻ CIL — Cognitive Interaction Layer CIL captures reasoning: why decisions were made what alternatives were considered how context influenced outcomes It makes reasoning persistent instead of ephemeral. ⸻ Timeline Layer All system changes are recorded as events: code changes AI outputs tool interactions architectural decisions This enables replay and evolution tracking. ⸻ Drift Detection Layer A continuous alignment system compares: current behavior vs PIK intent It detects: intent drift structural drift behavioral drift And produces measurable deviation signals. ⸻ 🔁 System Loop Contorium forms a continuous loop: PIK defines intent Execution produces behavior Timeline records evolution Drift system evaluates alignment Suggestions guide correction This creates a self-regulating project system. ⸻ 🧠 Key Insight Contorium is not an AI coding tool. It is a: Project Cognitive Runtime (PCR) A system where software projects maintain structured intelligence over time. ⸻ 🚀 Why it matters The bottleneck in AI development is no longer capability. It is continuity of understanding across: time tools agents sessions Conto

The goal is to start the day already briefed — not to spend the first hour becoming briefed. What follows isn't groundbreaking. It's just what pushing my own boundaries looks like in practice. The problem As a Tech Lead of a larger team, my mornings used to look something like this: open email, skim through multiple newsletters I subscribed to for staying current on AI and dev topics, switch to Slack, scroll through everything I missed, try to figure out what actually needs my attention, then check what code went into the repo in the last 24 hours. By the time I was done "catching up," a good chunk of the morning was gone. I knew there had to be a better way. Starting with Claude Cowork Claude's desktop app has a feature called Cowork, and within that, you can set up Scheduled tasks — automated tasks that run on a schedule. I set up two that run every morning: Newsletter digest: This one pulls in all the newsletters I received the day before and summarizes them for me, grouped by topic — AI-related first, then dev, then everything else. Instead of opening each email and scanning for what's relevant, I get a curated briefing in seconds. Slack summary: This gives me a full summary of yesterday's Slack conversations across channels, and more importantly, flags what actually needs my attention. No more scrolling through hundreds of messages trying to separate signal from noise. The only downside? The Claude desktop app needs to be open and running for these to kick in. It's not a dealbreaker, but worth knowing. I'll be honest — the idea wasn't entirely mine. When you set up a new Scheduled task in Cowork, a Daily Brief is literally the example they suggest. I just happened to already be poking around with something similar. A lucky coincidence. Taking it a step further with Claude Code One of the hardest parts of leading a larger team is keeping tabs on everything that changes in code. PRs get merged, features get shipped, bugs get fixed — and it's nearly impossible to

By the team at MailTester Ninja — a real-time email verification API that stores nothing. We verify a lot of email for a living. So we pointed our infrastructure at a representative panel of 50,000 of the world's most-linked domains and measured how email is actually configured in 2026 — MX providers, SPF and DMARC. Pure DNS, aggregate only, no personal data . Here's what the internet's mail setup looks like right now. Email is still (almost) everywhere 79.9% of these domains are mail-enabled (they publish MX records). Email isn't going anywhere. Authentication: adopted, but not enforced 75.8% publish an SPF record 64% publish a DMARC record …but only 22.6% actually enforce it with p=reject That last number is the real story. Of the domains that bother to publish DMARC, only 35.2% are on p=reject — the rest sit on p=none (37.2%, monitoring only) or quarantine (27.6%). Most of the web announces a policy it doesn't enforce. That's a deliverability and spoofing gap hiding in plain sight. Who runs the world's inboxes? Other / self-hosted — 32.6% Google Workspace / Gmail — 28.2% Microsoft 365 / Outlook — 22.5% Proofpoint — 5.5% Mimecast — 3.1% Tencent QQ — 2% Namecheap — 1.3% Cisco IronPort — 0.9% Self-hosted and the two hyperscalers (Google Workspace and Microsoft 365) dominate, but the long tail of providers is very real — which is exactly why deliverability is hard: every provider blocks, greylists and reputation-scores differently. Why we publish this We built an open, daily-updated dataset and a live dashboard because deliverability decisions should be based on data, not folklore. It's CC BY 4.0 — use it, cite it, build on it. Want to check a specific domain? Our free analyzer shows any domain's MX / SPF / DMARC in one click — no signup, nothing stored. Methodology: Live DNS scan (MX/SPF/DMARC). Aggregate only — no email sent, no personal data. Sample updated Wed, 01 Jul 2026 12:31:00 GMT.

Exhaust the enemy's strength without fighting. Weaken the strong by nurturing the soft. — The 36 Stratagems, " Wait at Leisure While the Enemy Labors " P flipped the business card over and wrote one letter on the back: P . Then P walked into the conference room. P didn't do opening lines. P doesn't have a name — not yet, not in this series anyway. But if you've read the earlier stories, you'd recognize the signature. The first story — P's own article got flagged as "low quality" by the company's AI moderation system. P dug into the internal API, pulled 347 flagged records — effective accuracy came out to 38%. More false positives than correct identifications. The second story — an AI payment gateway processing $2.8 billion. The CTO backed it with formal verification, claimed it was "mathematically bulletproof." P spent eight months quietly building an adversarial testing pipeline, and proved the gateway would approve illegal transactions. P won both times. P left zero fingerprints both times. After those two jobs, P stopped working for other people. This time, P got brought in as an independent evaluator. Two Companies, One Customer, Zero Questions The customer was a mid-sized industrial IoT firm called FirmCore . Their production-line gear had been running for almost a decade. The monitoring system was going down once a month, and management had finally had enough. They decided to bring in an AI monitoring platform. A good call — right up until they decided to run two vendors through POC at the same time and pick a winner. "We want to see who can actually cover our failure modes," the VP said in the meeting. "We've also brought in an independent evaluator." P was that evaluator. The two AI monitoring companies were MonitorAI and SentryWave . MonitorAI's pre-sales team went first, slides blazing with "99.3% fault coverage, validated across 3 manufacturing customers." SentryWave followed right behind: "99.7% coverage, 7-day deployment" — bigger numbers, bolder font.

It's the end of an era for the PlayStation store on PS3 and PS Vita, with Sony now planning to shut down its digital distribution service on both consoles. The PlayStation store on PS3 will close in select markets later this year, including Mexico, Honduras, and Nicaragua starting in August, with "additional Latin American and […]

most auto-apply tools have a dirty secret: they only autofill the form. they drop your details in and stop. some press submit. almost none read the confirmation the applicant tracking system sends back afterward, which means they cannot actually tell a click from a landed application. so they show you "applied" and hope. we read that confirmation. it is the whole point of what we build. and the side effect of reading it is that we have a status most tools do not: failed . a column that says, out loud, this one did not go through. having that column means we can be wrong out loud too. today we were. our apply agent clicked submit on a real Greenhouse form. the form went through. then, about half a second later, a downstream network blip threw an error, and the old code took that to mean the whole run had failed. it stamped a real, registered application as failed . a false negative on the one signal that matters most. the fix (in submitter.ts ) is a gate we now call submitClickIssued . once the agent has actually clicked submit, a later transport error can no longer produce a hard failed . it resolves to requires_human_review with a "likely landed, confirm this one" disposition instead. a blip after the click can no longer fake a failure. worst case, we ask you to double-check one, instead of lying to you in either direction. it is not a glamorous ship. no new feature, no screenshot. but a tool that never fails is a tool that never tells you, and the boring reliability days are the actual product. building this in public. no fabricated numbers, just the log.

4-Phase Orchestration: How 5 Universal Agent Skills Achieve YAML-Driven Rules + Composable Components + Graceful Degradation When you're hard-coding your 3rd scoring if-else, maybe it's time to ask: can I move the rules into YAML and let the business change config instead of code? The Problem: Why Do Agent Skills Keep Reinventing the Wheel? Every Agent developer faces the same dilemma — every business scenario rewrites a similar pipeline : Scoring: Extract features → Match rules → Calculate score → Generate report Complaints: Extract ticket → Cross-validate → Pinpoint root cause → Archive Querying: Understand intent → Build SQL → Execute query → Render chart The skeleton is identical. What changes is only the "content" at each step. Yet every team builds pipelines from scratch. teleagent-skills offers an answer: freeze the skeleton into 5 universal Skills with 4-Phase orchestration, and let business changes live in YAML config only . Architecture Overview: 4-Phase Pipeline + 5 Universal Skills 2.1 4-Phase Orchestration Diagram ┌─────────────────────────────────────────────────────────────┐ │ Upper Business Skill │ │ (Scoring Engine / Evidence Chain / Data Aggregator / ...) │ └──────────┬──────────┬──────────┬──────────┬────────────────┘ │ │ │ │ ▼ ▼ ▼ ▼ ┌──────────┐┌──────────┐┌──────────┐┌──────────┐ │ Phase 1 ││ Phase 2 ││ Phase 3 ││ Phase 4 │ │ Extract ││ Analyze ││ Generate ││ Archive │ │ ││ ││ ││ │ │Info- ││Data- ││Report- ││Archive- │ │Extractor ││Analyst ││Generator ││Manager │ └────┬─────┘└────┬─────┘└────┬─────┘└────┬─────┘ │ │ │ │ ▼ ▼ ▼ ▼ ┌─────────────────────────────────────────────────┐ │ JSON Contract (Structured Data Contract) │ │ phase1_output.json → phase2_input.json → ... │ └─────────────────────────────────────────────────┘ Core idea: each Phase is an independent component, and Phases pass data only through JSON contracts . Any Phase can be replaced (want a more powerful Analyzer? Swap it out) Any Phase can be skipped (degradation mode) Any Phase c

If you run a RAG system in production, you eventually hit a dilemma that has nothing to do with your model and everything to do with your cache. Cache the answers to save tokens and latency, and one day a source document changes — but your cache keeps cheerfully serving the answer it built from the old document. Nobody gets an error. The number is just quietly wrong. Cache nothing , and every single call re-retrieves the same chunks, re-reads them, and re-pays the full context bill to rebuild an understanding you already built five minutes ago for a nearly identical question. Stale or expensive. Most teams pick "expensive" because at least it's correct, then bolt on a TTL and hope. This post is about why the TTL doesn't save you, and about two specific, mechanical fixes that let you cache RAG context and stay fresh. I maintain an open-source library called Coalent that implements both, so I'll use it for the runnable examples — but the two ideas are portable and worth stealing even if you never pip install anything. Failure mode 1: the stale RAG cache (and why a TTL won't save you) Here's the standard "answer cache" sitting in front of retrieval: answer = cache . get ( query ) if answer is None : chunks = retriever . retrieve ( query ) answer = llm . synthesize ( query , chunks ) cache . set ( query , answer , ttl = 3600 ) return answer This works until billing.md changes. The refund window goes from 30 days to 14. Your cache has an answer keyed on "what is our refund policy?" that says 30, and it will keep saying 30 for up to an hour — or forever, if the same question keeps refreshing a TTL that never expires under load. The reason this is hard is that the cache key (the query) has no relationship to the thing that changed (the source). You cached an answer; you threw away the fact that this particular answer was derived from billing.md . So when billing.md changes, you have no way to find the answers that depended on it. The TTL is a confession that you can't answ

In this talk, Trisha identifies issues that slow down developers when writing, running and debugging tests, and look at tools that can help developers with each of these problems. There's live coding, analysis of social media poll results, an overview of solutions in this space, "best practice" recommendations, and machine learning will be mentioned at some point. submitted by /u/goto-con [link] [留言]

Most teams still suffer from a classic disconnect: the gap between what a developer is actually doing in their local Git repo and what Jira shows to the rest of the team. Developers forget (or delay) updating tickets not because they're lazy, but because switching context to Jira breaks flow. This leads to stale Jira boards, painful standups full of status checks, and poor visibility for PMs and Scrum Masters. I decided to close this gap with a tool called Jitly . The Core Idea Instead of manually doing: git pull origin main git checkout -b feature/ABC-123-xyz Go to Jira → Move ticket to In Progress Jitly lets you run jitly start ABC-123 and it handles everything automatically — including respecting team-specific branch naming conventions and commit message formats. Technical Implementation Highlights Jira Integration : Supports both Jira Cloud (SSO/OAuth2) and Jira Server/Data Center (PAT). Uses atlassian-python-api under the hood with proper session management. Git Operations : Uses GitPython + subprocess for safe operations. Handles dirty working tree by offering stash/push/discard choices. Workflow Hooks : Detects ticket status change (via manual command for now) and triggers git + Jira actions. Configuration : Team-level config for branch patterns (e.g. {type}/{ticket}-{slug}) and commit templates using Jinja2-style formatting. Idempotency : If a branch already exists for a ticket, it intelligently checks out and continues work instead of failing. Single binary-like experience via pip install with entry point. It's currently focused on the most painful part of the developer workflow — starting and finishing work — while keeping the tool lightweight. Would be interesting to hear how others are solving this Jira-Git synchronization problem in their teams. What tools/workflows do you use today? Download link: https://pypi.org/project/jitly/ Demo Video: https://www.youtube.com/watch?v=_czVTfqGKGY submitted by /u/Direct_Holiday_5934 [link] [留言]

HeroUI v3 is a redesigned React component library, previously NextUI, offering over 75 components, including 21 new ones, and a new React Native library with 37 components. Built on React Aria and Tailwind CSS v4, it emphasizes accessibility and customization. The library has experienced many updates since its release, and migration from the previous version is necessary. By Daniel Curtis

Sony has announced that from January 2028 it will entirely stop the production of physical discs for new PlayStation games. From that point onwards, new releases will only be available in digital versions from the PlayStation Store and other retailers. Games released before January 2028 will still be available on disc. "This is a natural […]

arXiv’s next chapter: Updates on our spin out from Cornell University: https://blog.arxiv.org/2026/06/30/arxivs-next-chapter/ submitted by /u/Nunki08 [link] [留言]

Rhythm Heaven isn't Nintendo's best-known series, nor its most prolific. Prior to the launch of Rhythm Heaven Groove on the Switch this week - it's out on July 2nd - there were only four previous entries, one of which was exclusive to Japan. The most recent came out more than a decade ago. Even still, […]

Smart speakers have spent the past few years searching for a compelling second act. Beyond music, timers, and controlling your lights, they've struggled to justify taking up space on the kitchen counter. AI promised to change that. Amazon debuted its new hardware powered by a revamped Alexa last fall, and now it's finally Google's turn. […]