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This article explores how Claude, Anthropic's language model, can handle up to 1 million tokens of context, providing a significant increase in its ability to understand and generate coherent long-form text compared to standard language models.

The article discusses the development of a new Linux kernel subsystem called 'Integrity Measurement Architecture' (IMA), which provides a secure way to measure and verify the integrity of system software components during the boot process. It highlights the importance of this feature in enhancing system security and integrity.

This article discusses the importance of Algolia DocSearch admin keys in managing search functionality for a website. It highlights the security implications of these keys and provides guidance on how to properly handle and protect them.

CanIRun.ai is a machine learning-powered platform that analyzes running form and provides personalized feedback to help runners improve their technique and prevent injuries. The platform uses computer vision and AI algorithms to analyze videos of runners and offer insights on their stride, cadence, and other biomechanical factors.

I know, it's a very first-world problem. But in my house, we have a hard time deciding what to watch. Too many options!

So I made this to recreate Cable TV for YouTube. I made it so it runs in the browser. Quickly import your subscriptions in the browser via a bookmarklet. No accounts, no sign-ins. Just quickly import your data locally.

I discovered this project because all-of-a-sudden Logi Options Plus software updater started taking 40-60% of my Intel Macbook Pro until I killed the process (of course it restarts). In my searches I ended up at a reddit discussion where I found other people with same issues.

I'm a minor contributor to this project but it aims to reduce/eliminate the need to use Logitech proprietary software and telemetry. We could use help if other people are interested.

Please check out the github link for more detailed motivations (eliminating telemetry) as a part of this project. Here is link: https://github.com/TomBadash/MouseControl

The article explores how to create a cursive handwriting effect in JavaScript, including techniques for generating realistic-looking cursive text and applying it to web pages. It provides code examples and explains the process of simulating the flow and variability of handwritten text using JavaScript and CSS.

Qatar's helium production shutdown has put the global chip supply chain on a two-week clock, as the country is a major supplier of this critical component for semiconductor manufacturing. This disruption could exacerbate the ongoing chip shortage and impact various industries that rely on semiconductors.

The article discusses a study that found the COVID-19 pandemic has led to significant declines in life expectancy in many countries, with the United States and England experiencing some of the largest reductions. The study highlights the profound impact the pandemic has had on population health globally.

The article introduces OpenTelemetry, an open-source observability framework, and its implementation in the Rust programming language. It covers the benefits of using OpenTelemetry for distributed tracing, metrics, and logs, and provides a step-by-step guide to setting up and using OpenTelemetry in a Rust application.

TUI Studio is a web-based platform that allows users to create, manage, and collaborate on interactive data visualizations and dashboards. The platform offers a range of tools and features to help users design, build, and share data-driven insights.

The article reports that Human Rights Watch (HRW) has condemned recent drone strikes in Haiti that killed several civilians, including children. HRW calls for an immediate investigation into the strikes and for the Haitian government to ensure the protection of civilians.

https://archive.ph/rP4cb (text at bottom)

https://x.com/elonmusk/status/2032201568335044978, https://xcancel.com/elonmusk/status/2032201568335044978

https://economictimes.indiatimes.com/tech/artificial-intelli...

https://futurism.com/artificial-intelligence/elon-musk-screw...

The article discusses the author's experience with the i-Ready educational platform, highlighting its strengths and limitations in supporting student learning and progress monitoring. It provides a balanced perspective on the platform's effectiveness and how it can be improved to better meet the needs of students and teachers.

The article discusses techniques for efficiently shipping grayscale photos at small scale, focusing on file compression, optimization, and delivery strategies to minimize file size and ensure fast loading times for users.

The article discusses using Thunderbird, a popular email client, as an RSS feed reader. It highlights the advantages of using Thunderbird for this purpose, such as its ability to manage multiple feeds and provide a unified interface for reading news and updates.

Researchers at Stanford University have recorded the first-ever heartbeat of a blue whale, the largest animal on Earth. The study provides new insights into the physiology and behavior of these massive marine mammals.

The article discusses how artificial intelligence (AI) could be used to help tackle climate change, with potential applications in areas like renewable energy, carbon capture, and climate modeling. Experts highlight both the opportunities and challenges of deploying AI for environmental sustainability.

Researchers at UNSW have developed a novel 'negative light' technology that can hide data transfers in plain sight, making it difficult for attackers to detect. The technology operates by manipulating the behavior of light to create invisible data channels, potentially enhancing the security of sensitive communications.

The article explores the transition from a production Ethereum network to a Merge Rehearsal (MR) network, using Kovan as an example. It discusses the technical and operational challenges involved in this process, as well as the benefits of running a Merge Rehearsal network to prepare for the Ethereum Merge.

We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window.

Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s.

Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M https://openai.com/index/introducing-gpt-5-4/).

Our solution uses small language models (SLMs): we look at model internals and train classifiers to detect which parts of the context carry the most signal. When a tool returns output, we compress it conditioned on the intent of the tool call—so if the agent called grep looking for error handling patterns, the SLM keeps the relevant matches and strips the rest.

If the model later needs something we removed, it calls expand() to fetch the original output. We also do background compaction at 85% window capacity and lazy-load tool descriptions so the model only sees tools relevant to the current step.

The proxy also gives you spending caps, a dashboard for tracking running and past sessions, and Slack pings when an agent is sitting there waiting on you.

Repo is here: https://github.com/Compresr-ai/Context-Gateway. You can try it with:

  curl -fsSL https://compresr.ai/api/install | sh

The full source code of Sweden's e-government platform was leaked from a compromised CGI Sverige infrastructure, potentially exposing sensitive government data and infrastructure details.

The article discusses how modern smartphones are essentially full-fledged computers with powerful capabilities, from processing and storage to the variety of sensors and connectivity options they offer. It highlights the evolution of smartphones and how they have become indispensable tools in our daily lives, serving as versatile computing devices beyond just making calls and sending messages.

Hey HN! We're Ashwin and Akshay from Spine AI (https://www.getspine.ai). Spine Swarm is a multi-agent system that works on an infinite visual canvas to complete complex non-coding projects: competitive analysis, financial modeling, SEO audits, pitch decks, interactive prototypes, and more. Here's a video of it in action: https://www.youtube.com/watch?v=R_2-ggpZz0Q.

We've been friends for over 13 years. We took our first ML course together at NTU, in a part of campus called North Spine, which is where the name comes from. We went through YC in S23 and have spent about 3 years building Spine across many product iterations.

The core idea: chat is the wrong interface for complex AI work. It's a linear thread, and real projects aren't linear. Sure, you can ask a chatbot to reference the financial model from earlier in the thread, or run research and market sizing together, but you're trusting the model to juggle that context implicitly. There's no way to see how it's connecting the pieces, no way to correct one step without rerunning everything, and no way to branch off and explore two strategies side by side. ChatGPT was a demo that blew up, and chat stuck around as the default interface, not because it's the right abstraction. We thought humans and agents needed a real workspace where the structure of the work is explicit and user-controllable, not hidden inside a context window.

So we built an infinite visual canvas where you think in blocks instead of threads. Each block is our abstraction on top of AI models. There are dedicated block types for LLM calls, image generation, web browsing, apps, slides, spreadsheets, and more. Think of them as Lego bricks for AI workflows: each one does something specific, but they can be snapped together and composed in many different ways. You can connect any block to any other block, and that connection guarantees the passing of context regardless of block type. The whole system is model-agnostic, so in a single workflow you can go from an OpenAI LLM call, to an image generation mode like Nano Banana Pro, to Claude generating an interactive app, each block using whatever model fits best. Multiple blocks can fan out from the same input, analyzing it in different ways with different models, then feed their outputs into a downstream block that synthesizes the results.

The first version of the canvas was fully manual. Users entered prompts, chose models, ran blocks, and made connections themselves. It clicked with founders and product managers because they could branch in different directions from the same starting point: take a product idea and generate a prototype in one branch, a PRD in another, a competitive critique in a third, and a pitch deck in a fourth, all sharing the same upstream context. But new users didn't want to learn the interface. They kept asking us to build a chat layer that would generate and connect blocks on their behalf, to replicate the way we were using the tool. So we built that, and in doing so discovered something we didn't expect: the agents were capable of running autonomously for hours, producing complete deliverables. It turned out agents could run longer and keep their context windows clean by delegating work to blocks and storing intermediary context on the canvas, rather than holding everything in a single context window.

Here's how it works now. When you submit a task, a central orchestrator decomposes it into subtasks and delegates each to specialized persona agents. These agents operate on the canvas blocks and can override default settings, primarily the model and prompt, to fit each subtask. Agents pick the best model for each block and sometimes run the same block with multiple models to compare and synthesize outputs. Multiple agents work in parallel when their subtasks don't have dependencies, and downstream agents automatically receive context from upstream work. The user doesn't configure any of this. You can also dispatch multiple tasks at once and the system will queue dependent ones or start independent ones immediately.

Agents aren't fully autonomous by default. Any agent can pause execution and ask the user for clarification or feedback before continuing, which keeps the human in the loop where it matters. And once agents have produced output, you can select a subset of blocks on the canvas and iterate on them through the chat without rerunning the entire workflow.

The canvas gives agents something that filesystems and message-passing don't: a persistent, structured representation of the entire project that any agent can read and contribute to at any point. In typical multi-agent systems, context degrades as it passes between agents. The canvas addresses this because agents store intermediary results in blocks rather than trying to hold everything in memory, and they leave explicit structured handoffs designed to be consumed efficiently by the next agent in the chain. Every step is also fully auditable, so you can trace exactly how each agent arrived at its conclusions.

We ran benchmarks to validate what we were seeing. On Google DeepMind's DeepSearchQA, which is 900 questions spanning 17 fields, each structured as a causal chain where each step depends on completing the previous one, Spine Swarm scored 87.6% on the full dataset with zero human intervention. For the benchmark we used a subset of block types relevant to the questions (LLM calls, web browsing, table) and removed irrelevant ones like document, spreadsheet, and slide generation. We also disabled human clarification so agents ran fully independently. The agents were not just auditable but also state of the art. The auditability also exposed actual errors in an older benchmark (GAIA Level 3), cases where the expected answer was wrong or ambiguous, which you'd never catch with a black-box pipeline. We detail the methodology, architecture, and benchmark errors in the full writeup: https://blog.getspine.ai/spine-swarm-hits-1-on-gaia-level-3-...

Benchmarks measure accuracy on closed-ended questions. Turns out the same architecture also leads to better open-ended outputs like decks, reports, and prototypes with minimal supervision. We've seen early users split into two camps: some watch the agents work and jump in to redirect mid-flow, others queue a task and come back to a finished deliverable. Both work because the canvas preserves the full chain of work, so you can audit or intervene whenever you want.

A good first task to try: give it your website URL and ask for a full SEO analysis, competitive landscape, and a prioritized growth roadmap with a slide deck. You'll see multiple agents spin up on the canvas simultaneously. People have also used it for fundraising pitch decks with financial models, prototyping features from screenshots and PRDs, competitive analysis reports and deep-dive learning plans that research a topic from multiple angles and produce structured material you can explore further.

Pricing is usage-based credits tied to block usage and the underlying models used. Agents tend to use more credits than manual workflows because they're tuned to get you the best possible outcome, which means they pick the best blocks and do more work. Details here: https://www.getspine.ai/pricing. There's a free tier, and one honest caveat: we sized it to let you try a real task, but tasks vary in complexity. If you run out before you've had a proper chance to explore, email us at founders@getspine.ai and we'll work with you.

We'd love your feedback on the experience: what worked, what didn't, and where it fell short. We're also curious how others here approach complex, multi-step AI work beyond coding. What tools are you using, and what breaks first? We'll be in the comments all day.

The article discusses Senator Ron Wyden's concerns about the NSA's activities under Section 702 of the Foreign Intelligence Surveillance Act, which allows the government to conduct surveillance on non-US citizens abroad. Wyden warns that the public will be 'stunned' by the NSA's actions in this area, which he believes violate privacy rights.

https://xcancel.com/id_aa_carmack/status/2032460578669691171

Hi HN, we’re Lewis and Edgar, building Captain to simplify unstructured data search (https://runcaptain.com). Captain automates the building and maintenance of file-based RAG pipelines. It indexes cloud storage like S3 and GCS, plus SaaS sources like Google Drive. There’s a quick walkthrough at https://youtu.be/EIQkwAsIPmc.

We also put up this demo site called “Ask PG’s Essays” which lets you ask/search the corpus of pg’s essays, to get a feel for how it works: https://pg.runcaptain.com. The RAG part of this took Captain about 3 minutes to set up.

Here are some sample prompts to get a feel for the experience:

“When do we do things that don't scale? When should we be more cautious?” https://pg.runcaptain.com/?q=When%20do%20we%20do%20things%20...

“Give me some advice, I'm fundraising” https://pg.runcaptain.com/?q=Give%20me%20some%20advice%2C%20...

“What are the biggest advantages of Lisp” https://pg.runcaptain.com/?q=what%20are%20the%20biggest%20ad...

A good production RAG pipeline takes substantial effort to build, especially for file workloads. You have to handle ETL or text extraction, chunking, embedding, storage, search, re-ranking, inference, and often compliance and observability – all while optimizing for latency and reliability. It’s a lot to manage. grep works well in some cases, but for agents, semantic search provides significantly higher performance. Cursor uses both and reports 6.5%–23.5% accuracy gains from vector search over grep (https://cursor.com/blog/semsearch).

We’ve spent the past four years scaling RAG pipelines for companies, and Edgar’s work at Purdue’s NLP lab directly informed our chunking techniques. In conversations with dozens of engineers, we repeatedly saw DIY pipelines produce inconsistent results, even after weeks of tuning. Many teams lacked clarity on which retrieval strategies best fit their data.

We realized that a system to provision storage and embeddings, handle indexing, and continuously update pipelines to reflect the latest search techniques could remove the need for every team to rebuild RAG themselves. That idea became Captain.

In practice, one API call indexes URLs, cloud storage buckets, directories, or individual files. Under the hood, we’re converting everything to Markdown. For this, we’ve had good results with Gemini 3 Pro for images, Reducto for complex documents, and Extend for basic OCR. For embedding models, ‘gemini-embedding-001’ performed reasonably well at first, but we later switched to the Contextualized Embeddings from ‘voyage-context-3’. It produced more relevant results than even the newer Voyage 4 models because its chunk embeddings are encoded with awareness of the surrounding document context. We then applied Voyage’s ‘rerank-2.5’ as second-stage re-ranking, reducing 50 initial chunks to a final top 15 (configurable in Captain’s API). Dense embeddings are just half the picture and full-text search with RRF complete our hybrid retrieval. In the Captain API, these techniques are exposed through a single /query endpoint. Access controls can be configured via metadata filters, and page number citations are returned automatically.

The stack is constantly changing but the Captain API creates a standard interface for this. You can try Captain, 1 month for free, and build your own pipelines at https://runcaptain.com. We’re looking for candid feedback, especially anything that can make it more useful, and look forward to your comments!

The article discusses a new method for detecting fake social media accounts using behavioral patterns. The approach analyzes user activity, network connections, and content to identify automated or inauthentic accounts, aiming to help combat the spread of misinformation and manipulation on social media platforms.