标签：#Go

Overview AGENTS.md is useful. It gives AI coding agents a place to find repo-specific guidance: how to behave what conventions matter what areas need extra caution what kinds of changes should trigger review That is a meaningful improvement over sending an agent into a repo with no instructions at all. But AGENTS.md is not enough. It can tell an agent to be careful. It cannot, by itself, make execution safe, verification trustworthy, or review inspectable. For that, a repository needs more than instructions. It needs: declared safe commands a canonical verification path receipts that show what actually ran That is the difference between agent guidance and execution governance. Instructions Help. They Do Not Govern Execution. An instruction file is still prose. That means it can express intent, but it does not automatically create operational truth. For example, AGENTS.md can say: run the right checks before handoff avoid destructive commands do not edit generated files ask before touching infrastructure Those are good rules. But notice what they leave unresolved: which checks are the right ones which commands are actually safe which paths are protected structurally versus only suggested what should count as evidence that verification happened how to tell whether a failure came from code, setup, or drift That is where many agent workflows still break down. The agent may follow the spirit of the instructions and still take the wrong execution path. Safe Commands Need To Be Explicit One of the biggest gaps in agent-oriented repos is that they often declare guidance without declaring a safe command surface. The repo may tell the agent: Run tests before you finish. But that still leaves a dangerous amount of interpretation. Which task is safe? Is it: npm test pnpm test make check docker compose run test a narrower unit-test path the CI workflow itself And if several exist, which one is canonical for a routine code change? The repo should not force the agent to infer that

Tenor still connects to Google apps, but other platforms must look elsewhere for GIFs.

Google's NotebookLM is adding a new way to catch up on your notes: TikTok-style AI videos. The new feature is rolling out to Google AI Ultra and Pro subscribers, allowing NotebookLM to generate 60-second vertical AI clips based on the sources you upload to the app. The example shared by Google details Australia's unsuccessful war […]

Google is updating its image generator to make it faster and cheaper, making it a more useful tool for creators looking to make AI content.

They may not look as good, but Nano Banana 2 Lite images only take a few seconds to create.

My learning so far I absolutely love learning to code. I gave it up to vibe code earlier last year and completely regret it. At first it felt like I was moving faster, but over time I realized I was skipping the part that actually made me better. My learning journey is fueled by passion and the hopes to move into a Go/SWE/Cloud type role. I do not know exactly how I will go about doing so, but I will work until I am noticed. Right now I am trying to focus on building real understanding. Not just getting something to work, but knowing why it works. I want to be able to read errors, debug my own code, understand the tools I am using, and slowly become the kind of developer that can solve problems without panicking. Learn to code! If anyone has any doubts on if coding is "worth it" still, I can account for how personally fulfilling it is. Solving a bug/problem in your own code gives me a personal high. There is something different about struggling with something, walking away, coming back, and finally seeing it click. It reminds you that you are actually learning. Every small fix feels like proof that you are getting better. I am not against using tools or AI. I still think they can be helpful. But I do think there is a big difference between using them to learn and using them to avoid learning. I had to learn that the hard way. So if you are new, or if you stopped for a while like I did, I really think you should keep going. Build small things. Break stuff. Fix it. Read docs even when they are boring. Ask questions. Take notes. Let yourself be bad at it for a while. I do not know where this journey will take me yet, but I know I want to keep showing up.

Explore how the Open Source Program Office uses GitHub’s new license compliance product to manage open source dependencies at scale. The post How GitHub maintains compliance for open source dependencies appeared first on The GitHub Blog .

The GIF-picking interfaces in some of your favorite online platforms might look different going forward, as Google prepares to shut down the Tenor API today. While the Tenor website, along with its searchable GIF library, will remain live, platforms like X, Discord, Bluesky, and WhatsApp that previously integrated the API are now having to migrate […]

You can use Gemini to quickly search your Gmail inbox with natural language.

Cost is everything. In just about every agentic conversation, the three things that come up for enterprises implementing AI workloads are: Cost Observability Security and as AI continues to throw everyone for a loop when it comes to cost management (e.g - Uber running out of the yearly token budget in one quarter), the ability to shrink resource (like hardware) usage will be crucial moving forward. In this blog post, you will learn how to cust costs by 90% using Agent Susbtrate in comparison to Agents running in k8s Deployments/Pods. The Cost Comparison Agents need a place to run. The "place to run" needs to be a platform that's easily managed, orchestrated, and has the ability to cluster resources. Resources like CPU, GPU, and memory need to be able to scale and expand. Without this, it's a matter of manually managing servers that Agents are running on and clients to interact with said server. That's why so many organizations choose Kubernetes to run Agentic. When running Agents per Pod, however, that can get costly very quick in terms of hardware (GPU, CPU, memory) and performance (can your cluster scale up and down quickly based on resource needs when it comes to Agents coming up and going down per use?). The tests in this blog post show: Always-on Agents running in k8s. Actors running in Workers via Agent Substrate And the comparison will be 50 always-on Pods in comparison to 50 Actors across 5-7 Workers (Pods). If there are 50 Agents running per Pod and 50 Agents running per Worker with 5-10 Actors per Pod, you can already imagine the hardware resource savings that can be accomplished. Right now, the majority of organizations start off with the "one Agent per Pod" approach as that's the fastest way to show value and get up and running. For the future, however, Agents in Actors via Agent Substrate will be how organizations deploy when they care about efficiency, optimization, and managing cost. Let's dive in from a hands-on perspective. Prerequisites To follow a

The Financial Times has a good article on how AI is changing the capabilities of video surveillance, with information from both Israel/Iran and Russia. I wrote about this sort of thing a few years ago, how AI enables mass spying in the way that computers and networks enabled mass surveillance. The interesting development in the article is that AI allows people to ask natural language questions about video footage to AIs—and AIs can answer them. In contrast with older tools restricted to a few dozen preset searches, these new tools allow an almost unlimited range of enquiries by enabling language-based searches on video...

I have been working on Glaze , a small desktop WebView toolkit for Go. The short version: Glaze lets a Go program open a native desktop window backed by the WebView already available on the operating system, without using CGo. It currently targets: macOS, through WKWebView Linux, through WebKitGTK Windows, through WebView2 The project is still young, but the core idea is already useful: keep small Go desktop tools close to the normal Go workflow. No C compiler in the build path. No bundled native helper library. No large application framework around it. Just Go code calling the system WebView. Why I wanted this I write a lot of small tools in Go. Some of them are fine as CLI programs. Others need a basic interface: a form, a preview, a local dashboard, a small editor, or a way to inspect and manipulate data visually. For those cases, HTML is often enough. The browser gives me layout, text rendering, forms, tables, keyboard handling, and a familiar debugging model. But I do not always want to ship a web server as the user interface. I also do not always want to pull in a large desktop framework when all I need is a native window around a local UI. A WebView is a reasonable middle ground. The problem is that many WebView solutions eventually bring CGo, native build tooling, helper libraries, or larger framework assumptions into the project. That is not necessarily wrong. For many applications, those trade-offs are acceptable. For this project, I wanted something narrower. The design constraint The main constraint behind Glaze is simple: Use the WebView already provided by the OS, but call it from Go without CGo. Glaze uses purego to call native platform APIs directly from Go. That means each backend talks to the platform WebView: WKWebView on macOS WebKitGTK on Linux WebView2 on Windows The result is not a full GUI toolkit. That is intentional. Glaze is focused on the window, the WebView, JavaScript-to-Go bindings, and a few desktop helpers that are useful for small t

The interesting part of OpenAI and Codex on AWS is not that another cloud menu got more model names. That part is useful. Enterprises want strong models. Developers want Codex closer to their infrastructure, data, and deployment machinery. The interesting part is that frontier AI is being pulled into the same boring machinery that already governs everything else companies run: procurement, IAM, billing commitments, region policy, audit logs, support contracts, data boundaries, and security review. That sounds like paperwork. It is also how enterprise software becomes real. model access was the easy problem For a while, AI adoption was framed as an access problem. Can we call the model? Can we get enough rate limit? Can we wire the SDK into our product? Can the coding assistant see enough of the repo to be useful? Those are real questions. They are not the end of the story. The next set is much more familiar to anyone who has operated software inside a company: which account owns this usage, which data can cross the boundary, who can create agents, which region runs inference, how the bill is allocated, and what evidence exists when an incident involves model output. That is the part where the demo becomes a platform. OpenAI on AWS matters because many companies already have that platform muscle in AWS. They have IAM, billing, private networking, audit trails, procurement paths, compliance evidence, cost allocation tags, and teams whose job is to make all of this survivable. Putting a frontier model behind that machinery does not make the hard parts disappear. It makes them legible. bedrock is a procurement surface Amazon Bedrock is usually described as a managed model service, which is true and also undersells the point. For enterprises, Bedrock is a procurement and control surface. If OpenAI models and Codex are available through Bedrock, a company can route adoption through an existing cloud relationship instead of creating a new vendor path for every team that wa

Starting in IT I started in IT at a local school in my small town in December 2024. It was my first job out of college after earning my B.S. in Cybersecurity. None of the infrastructure was updated. Everything was failing, and luckily, I had one other IT person there: my director. I honestly think he knew less than I did, and he would get frustrated at almost every ticket. Even then, I knew I wanted a role where I could code. Fast forward to more recently, he had a freak out and quit. Now we have a two-person team, and everything is finally up to date and functioning. Even with things improving, I still knew I wanted to move toward a SWE-type role. Learning to Code I first decided to learn C# for Unity. I was really into game dev while I was in college, so it felt like a natural place to start. I began with the Microsoft/freeCodeCamp C# certification, and I surprisingly really enjoyed it. I made a few small games on itch.io that no one cared about, but I had fun building them. After that, I went on a bit of a language-hopping spree. I jumped from C# to C++, then into full-stack web development. I actually stuck with web dev for a while and really enjoyed it. But this cycle went on for awhile of just constant swapping. Wannabe Founder Then something switched overnight. I went from writing maybe 0-5% AI-generated code to using AI for nearly everything. I started spam-building startup ideas that did not really go anywhere. I may have made around $2-3k from them, but most of the time I was just chasing money and building whatever I thought had the quickest path to making some. I got seriously addicted to vibe coding. I tried Codex, Cursor, Claude, and basically anything with AI in it. I did like Codex the most, though. Eventually, I realized I had almost completely stopped coding by hand. I was not passionate about the startup ideas I was building. I loved coding, and I knew I had to step back. Back to Coding Now I am back to coding without AI assistance. I will eventua

SCOTUS falls short of deeming geofence warrants unconstitutional, though.

Google expands personalized intelligence to Gemini app image creation

The EU wants Google to share search data with competitors and open up AI on Android, but Google alleges major privacy risks.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool—one that your company nonetheless calls Alex, an…

The Supreme Court's decision to limit geofence warrants is a win for privacy advocates, who called their use unconstitutional but sought an outright ban.

Interesting research on a new class of weak RSA keys: keys with lots of zeros. It turns out that these keys are out in the wild. The badkeys project is an open-source service that checks public keys for known vulnerabilities. While developing this tool, Hanno collected a massive number of real-world keys from public sources, including Certificate Transparency logs, internet-wide TLS and SSH scans, PGP keys, and many others. By searching this dataset for unexpectedly sparse RSA moduli, we uncovered a large number of keys in the wild with the patterns in Figure 1...