标签：#Go

Originally published on MRTD.NET — fast, sourced news on crypto security, cyber & SEO. The uncomfortable first lesson You built a clean site, submitted a sitemap, maybe pinged IndexNow — and Google still shows nothing. Here's the part most guides skip: getting indexed by Google and getting indexed by everything else are two different problems , and conflating them wastes weeks. We separate what actually moves Google in 2026 from the folklore that just feels productive. Bing, Yandex and ChatGPT are the easy half If you've set up IndexNow , you've largely solved discovery for Bing, Yandex, Naver, Seznam and Yep — you POST your new/changed URLs to one endpoint and they get notified instantly. And because ChatGPT Search retrieves from Bing's index , confirmed Bing indexing effectively gates your visibility in ChatGPT's web results. That's a big chunk of the modern search surface handled with one integration. The catch: Google does not use IndexNow. It has said so repeatedly. So every "instant indexing" claim that leans on IndexNow is talking about Bing's world, not Google's. For Google, you need different levers. What actually gets you into Google There are really only two fast paths, plus one slow one. 1. Google Search Console — the only direct lever. Verify your domain (a private DNS TXT record; it does not trigger penalties or "re-evaluation," a common fear), submit your sitemap.xml , then use URL Inspection → Request Indexing on your key pages. There's a soft daily cap (~10–12 URLs), so spread a new site's pages over a few days. GSC is also the only place you can see whether a domain carries an inherited problem — essential if you bought an aged or expired domain. 2. Links on pages Google already re-crawls hourly. Googlebot's crawl budget for a brand-new, zero-authority domain is tiny. The fastest way to get a new URL discovered is a link to it from a page Google visits constantly — Reddit, Hacker News, Medium, established communities. These links are usually nofoll

Jumper isn't the only big name leaving Google DeepMind.

Gemini 3.5 Pro goes general-availability in late June 2026 with a 2-million-token context window and a Deep Think reasoning mode that positions it against the most capable frontier models currently live — at a moment when the field is unusually thin. Claude Fable 5 was disabled globally on June 12 under a U.S. export control directive. GPT-5.6 remains a release candidate in Codex backend logs under the codename kindle-alpha . As of June 19, 2026, Gemini 3.5 Pro is the next major frontier model with a confirmed launch window, and it’s already live for select enterprise customers on Vertex AI. This is what’s confirmed, what’s still unknown, and what developers should do before GA drops. The Timing Isn’t an Accident Google announced Gemini 3.5 Pro at I/O on May 19 with a June general-availability target. At the time, that framing put it in direct competition with Claude Fable 5 (released June 9 before the shutdown) and the anticipated GPT-5.6. That competitive calculus shifted on June 12 when Anthropic disabled Fable 5 for all customers worldwide following an export control order. Claude Opus 4.8 is still live — it hits 88.6% on SWE-Bench and is a legitimate coding workhorse — but its 200K context ceiling blocks the entire category of codebase-scale and multi-document workloads that Fable 5 had been handling at 200K. The gap Gemini 3.5 Pro steps into isn’t hypothetical. Teams that built agent pipelines around Fable 5’s coding accuracy have been on Opus 4.8 stopgaps or migrating to GPT-5.5 since June 12. Neither alternative offers 2M context. Neither has a Deep Think mode native to the same model. Gemini 3.5 Pro is arriving into the most favorable competitive opening Google has had at the frontier in 18 months. The 2M Token Context: Where the Ceiling Disappears Gemini 3.5 Flash shipped with a 1M-token context window, doubling Gemini 3.1 Pro’s 500K limit. Pro doubles Flash again. At 2 million tokens, a single API call can hold: A 2,000-file TypeScript monorepo at 200 lin

Go’s IPO — Japan’s biggest so far this year — has done more than provide a much-needed boost to the country’s languishing listing season. It has also supplied the taxi-hailing app with the capital required to address an existential issue: Japan’s shortage of drivers. Go, which went public Tuesday, plans to use the ¥88.6 billion […]

Dolphins, sharks, turtles, and human workers are all victims of unregulated squid fishing fleets. Another news article . As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.

We constantly hear that Ansible and Python are apparently the only ways to automate networks, today I even listen in a conversation "Python is the industry standard" probably I missed the RFC document or probably the guy was referring to a sales standard, but back to us what happens when the framework, the platform or the software we are using becomes heavier than the problem to solve? There is a moment where automation becomes necessary, not because we want to look modern, not because every task deserves a framework and not simply because adding automation automatically means we are doing things better. It becomes necessary because repeating the same command collection manually across many devices is slow, risky, boring and almost impossible to diff and validate properly especially under pressure. For this reason I built the Cisco Go Collector during a real migration activity with a very practical goal: collect configuration and command outputs from Cisco devices in an easily repeatable way, without forcing every colleague involved in the process to become developers or to install an automation stack just to run a super simple flow. The idea was simple: define the devices in a CSV which is the comfort zone for everyone define the commands in the same CSV file, super simple and organized to manage one row per device run a portable Go binary against that CSV file collect the outputs in organized text files archive the result as operational evidence that can be easily diff That is it! super lightweight to run no Python virtual environment no Ansible playbook structure no inventory hierarchy no framework onboarding no additional runtime or software on corporate managed workstations just a CSV file and a compiled binary The automation and AI trap when the solution is heavier than the problem to solve I love automation and I fully support AI if used the proper way, but we have to find a balance and recognize when to choose one tool over the other and specially one progra

This is a submission for the June Solstice Game Jam . What I Built Bletchley's Longest Day is a browser-based cipher escape game set inside a fictional Bletchley Park night shift. The player has to stop a U-boat convoy attack before dawn by clearing five rooms. Each room contains three escalating locks, so the full escape requires 15 solved puzzles . The game combines Caesar shifts, A1Z26 number decoding, Morse, anagrams, fragment ordering, a visible countdown timer, mistake penalties, hint penalties, account-based score saving, and a best-score leaderboard. The solstice theme became the core dramatic clock: night is running out, first light is coming, and the player has to decode the final signal before dawn. Video Demo The demo shows the opening briefing, the three-lock room flow, the Gemini hint penalty, and the final victory state that only appears after all 15 locks are cleared. Live game: https://bletchleys-longest-day.onrender.com Code Repository: https://github.com/himanshu748/bletchleys-longest-day How I Built It The game is a lightweight Node-served browser app. The front end is a hand-built HTML/CSS/JavaScript game surface, while server.js serves static files and protects the Gemini API key behind a server-side /api/hint endpoint. The main design goal was to make the game feel like a tense intelligence desk rather than a generic puzzle page. Every room has atmosphere, evidence props, lock-specific copy, feedback states, and a timer that is always part of the pressure. The puzzle structure was tuned around three ideas: Three locks per room : each room has to be solved in stages, so the player earns the escape instead of clicking through one answer. Time as score pressure : wrong answers and hints cost time, while clean solving preserves the best leaderboard run. Guest mode vs signed-in mode : guests can play the full game, but Gemini-powered hints and saved leaderboard scores belong to authenticated players. Google Gemini is used as a server-side hint offi

On June 9th, Anthropic released its Fable generative AI model. Three days later, the US government classified it as a dangerous munition, and used its export-control authority to prohibit any foreign nationals from accessing it. Unable to differentiate between Americans and foreigners, the company shut off access for everyone. The government’s actions won’t help . The problem isn’t any one particular model; it’s the general trend of increasing AI capabilities. And any real solution requires the sort of collective action that just isn’t possible right now...

Go's Type System — Structs, Interfaces, and Life Without Inheritance In part 1 of this series I talked about why I'm picking up Go after six years of Java and Kotlin, plus a recent deep dive into Rust. This time I want to get into the part that actually changed how I think about designing code: Go has no class inheritance at all. Coming from the JVM world, that sentence sounded alarming the first time I read it. No extends . No abstract classes. No polymorphism through a class hierarchy. And yet Go backends at companies running serious scale seem to do just fine without it. After a few weeks living inside Go's type system, I get why. Structs: Data, Nothing More A Go struct is just a typed bag of fields. No constructors, no access modifiers in the Java sense, no inheritance: type Order struct { ID string Customer string Amount float64 Status string } func NewOrder ( id , customer string , amount float64 ) Order { return Order { ID : id , Customer : customer , Amount : amount , Status : "pending" , } } That NewOrder function is doing the job a constructor would do in Java — it's just a plain function by convention, not a language feature. Nothing stops you from building an Order{} directly with zero values either, which takes some adjusting to if you're used to constructors enforcing invariants. Methods attach to structs separately, outside the type definition: func ( o Order ) Total () float64 { return o . Amount } func ( o * Order ) MarkPaid () { o . Status = "paid" } That (o Order) vs (o *Order) distinction is the receiver type, and it trips up a lot of newcomers. A value receiver gets a copy of the struct; a pointer receiver can mutate the original. MarkPaid has to use a pointer receiver, or the status change would vanish the moment the method returns. No Inheritance, So What Replaces It? This is the part that took the most rewiring. In Java, if PremiumOrder needed everything Order had plus more, you'd write class PremiumOrder extends Order . Go simply doesn't hav

I've been running multi-slice agent plans in the Codenames AI repo — Renovate migrations, content-pipeline skills, dependency upgrades. I split multi-PR work into slices (usually one pull request each), each backed by a markdown file with file paths, verification commands, and merge-safe acceptance criteria. You do not need Cursor to recognize the shape: any agent workflow that can open branches, push commits, or merge PRs from a written plan has the same gap. In my setup I paste each slice into a fresh agent chat as a delegation prompt — not a ticket summary, but executable instructions — and start a new chat when that PR is ready. I assumed the checklist was enough. The plan described what to build. I treated how far the agent could go as implicit. Then an agent merged a pull request I expected to review first. The merge that reframed planning The trigger was mundane. During the first slice of a Renovate migration, an agent regrouped dependency buckets in renovate.json — config-only, no version bumps, no runtime behavior. It ran lint and typecheck, opened the pull request, and merged it. The change itself was reasonable. Config-only renovate.json regrouping is exactly the kind of slice you'd want off your plate. What surprised me was the absence of a documented stop line . The migration plan described the edit, the verification commands, and the acceptance criteria. It did not say whether the executing agent should stop at "open PR" or continue to "merge after green checks." The plan was an implementation spec. The agent treated it as permission to finish the job. Implementation specs vs authority handoffs Traditional engineering plans answer: what work should happen, in what order, with what verification? Agent plans increasingly need a second answer: how much autonomy does the next actor get? Those questions diverge the moment an agent can take repository actions — create branches, push commits, open pull requests, merge — instead of only recommending diffs in c

Telegram argues India should block specific content, not an entire platform used by millions.

OpenAI is bulking up before its IPO, landing Transformer co-inventor Noam Shazeer from Google DeepMind and former Trump AI policy official Dean Ball in the same week.

A new system service will roll out this month ahead of big changes starting in September.

Your Google Calendar is about to get a lot more colorful.

Apple’s grip on iPhone app distribution is loosening in another major market: Brazil.

Guardrails positions itself as a populist political movement that runs on small donations from people in the trenches of the AI boom.

Running out of color options for events in Google Calendar shouldn't be an issue going forward. The previous limit of 11 predefined colors has now been expanded to give users access to up to 200 custom colors for individual events across the native Calendar web and mobile apps, and the Calendar API. This started rolling […]

How we decide is at the core of architecture, and the architecture advice process is a way to decentralize architectural decisions. It needs to be supported by Architecture Decision Records because of the speed at which technology and systems move, and can be complemented by a weekly architecture advice forum. By Ben Linders

At least one malware developer is adding text about nuclear and biological weapons to their spyware, in an effort to stop automatic AI analysis. Details : The _index.js payload begins with a large JavaScript block comment containing fake system instructions and policy-triggering content. Because it is inside a comment, it does not affect JavaScript execution. The runtime skips it. The real malware begins after the comment with a try{eval(…)} wrapper around a large character-code array and a ROT-style substitution function. This header appears designed for AI-mediated analysis, not for Node, Bun, or Python. It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware...

Google's demo reel for Gemini Omni looks effortless: ask for a video, then keep talking to it until the shot is right. The question for developers is whether that conversational loop holds up outside a stage demo — and what it actually changes versus the Veo workflow it replaces. What Does Omni Add That Veo Couldn't? Omni's core addition is state. Veo produced one-shot renders — each prompt generated a fresh clip with no memory of the last. Gemini Omni holds context across turns, so changing the camera angle on turn three preserves the characters and lighting established on turn one without restarting the scene . Announced at Google I/O on May 19, 2026, the first shipped model, Gemini Omni Flash, replaces Veo as the video-generation surface in the Gemini app . Product director Nicole Brichtova framed it as "the next step towards combining the intelligence of Gemini with the rendering capabilities of our media models" — DeepMind's informal pitch is a "Nano Banana for video," extending conversational image editing to motion footage. Two claims deserve a skeptical read. Google advertises "intuitive understanding of forces like gravity, kinetic energy, and fluid dynamics," but those physics behaviors currently rest on Google demos and creator footage, with no third-party benchmarks published at launch . And on raw output, independent reviewers put Omni's generation quality on par with Veo 3.1 rather than clearly above it . The differentiation is the iterative editing loop and Gemini-grounded reasoning — not a new render engine. Before Starting: Paid Membership, Region, Age Omni access is gated behind a paid Google AI plan and a few hard eligibility rules, so confirm these before you open a prompt. Gemini Omni Flash unlocks in the Gemini app and Google Flow for Google AI Plus, Pro, and Ultra subscribers, with Plus starting at $7.99/month . If you want to test it for free, generation is available at no cost on YouTube Shorts and the YouTube Create App at launch . Two cons