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The article explores the fascinating question of why horses have evolved to have such large heads compared to their bodies, suggesting that this may be related to their social and cognitive abilities, which allow them to navigate complex social hierarchies and communicate effectively.

This paper proposes a new machine learning model for image classification that outperforms current state-of-the-art methods on several benchmark datasets. The model utilizes a novel neural network architecture and training approach to achieve superior performance.

The article discusses the use of icons in menus, highlighting the benefits and drawbacks of this design choice. It explores how icons can enhance usability and visual appeal, while also potentially introducing ambiguity or accessibility issues if not implemented thoughtfully.

The article analyzes the Jepsen test results for the NATS 2.12.1 distributed messaging system, focusing on its consistency and durability guarantees under various failure scenarios, such as network partitions, message redelivery, and message ordering.

The article explores the history and cultural significance of automats and cafeterias in New York City, once popular dining establishments that provided affordable, convenient, and innovative food service before the rise of fast-food chains.

https://www.data.jma.go.jp/multi/quake/quake_detail.html?eve...

https://www3.nhk.or.jp/nhkworld/en/news/weather-disaster/tsu...

https://earthquake.usgs.gov/earthquakes/map/?currentFeatureI...

https://www.tsunami.gov/?p=PHEB/2025/12/08/25342050/2/WEPA40

The article provides an overview of the author's personal website, which covers a range of topics including programming, technology, and personal reflections. It offers a glimpse into the author's diverse interests and experiences.

The article discusses techniques for debugging AMD GPU issues, including using the Radeon GPU Profiler, inspecting driver logs, and leveraging tools like RenderDoc and PIX to analyze GPU performance and identify potential problems.

This article discusses running Tailscale, a VPN software, on a jailbroken Amazon Kindle device. It covers the process of installing Tailscale on a jailbroken Kindle and the benefits it provides, such as secure remote access and the ability to use the Kindle as a network gateway.

IBM has announced plans to acquire Confluent, a leading provider of event streaming and data integration platforms. The acquisition aims to help IBM expand its hybrid cloud and AI capabilities, allowing customers to modernize their data infrastructures and drive business value from real-time data.

The article explores the efforts of researchers and analysts to locate and map North Korea's secretive fiber optic cable infrastructure, which is crucial for the country's communication and internet connectivity, despite its isolation from the global internet.

Microsoft is increasing the prices of its Microsoft 365 and Office 365 commercial subscriptions, effective March 1, 2023. The price changes are intended to reflect the increased value and expanded capabilities of the Microsoft 365 and Office 365 suites over time.

fanfa.dev is a website that provides free and open-source tools and resources for web development, including a code editor, task runner, and package manager. The site aims to simplify the development workflow and empower developers with a user-friendly platform.

The article explores the impact of cloud computing and open-source software on the cost of software development, suggesting that the cost has dropped by as much as 90% in recent years. It examines how these technological advancements have made software more accessible and affordable for businesses and individuals.

The article provides a comprehensive guide on latency profiling in Python, covering various techniques and tools to measure and analyze the performance of Python applications, including the use of libraries like cProfile, line_profiler, and pyinstrument.

The article examines the impact of information disclosure on financial markets, finding that increased transparency can lead to more efficient market pricing and better investment decisions, but may also result in increased market volatility and risk-taking.

Previously: Netflix to Acquire Warner Bros - https://news.ycombinator.com/item?id=46160315 (1333 comments)

The article explores the challenges and potential solutions for verifying the authenticity of AI-generated content, particularly in the context of the rapidly evolving field of AI. It highlights the need for robust verification methods to maintain trust and integrity in AI-powered communications and media.

The article explores a series of tricks and techniques used in creating tiny GLSL demos, including the use of bitwise operations, tricks for cheap trigonometry, and techniques for procedural textures and patterns.

Hi HN, I am Arlan and I am building Nia (https://trynia.ai), a context layer for AI coding agents. Nia lets tools like Cursor, Claude Code, and other MCP clients index and query real codebases and documentation so they stop hallucinating against outdated or wrong sources, with applications beyond coding agents to any AI system that requires grounded context across domains.

Coding agents are only as good as the context you give them. General models are trained on public code and documentation that is often old, and they usually have no idea what is inside your actual repo, internal wiki, or the exact version of a third party SDK you use. The result is very familiar: you paste URLs and code snippets into the prompt, the agent confidently uses an outdated API or the wrong framework version, and you spend more time verifying and correcting it than if you had written the code yourself. Once models are good enough at generating code, feeding them precise, up-to-date context becomes the bottleneck.

I ran into this pattern first on my own projects when (a few months ago) I was still in high school in Kazakhstan, obsessed with codegen tools and trying every coding agent I could find. I saw it again when I got into YC and talked to other teams who were also trying to use agents on real work.

The first version of Nia was basically “my personal MCP server that knows my repos and favorite doc sites so I do not have to paste URLs into Cursor anymore.” Once I saw how much smoother my own workflow became, it felt obvious that this should be a product other people could use too.

Under the hood, Nia is an indexing and retrieval service with an MCP interface and an API. You point it at sources like GitHub repositories, framework or provider docs, SDK pages, PDF manuals, etc. We fetch and parse those with some simple heuristics for code structures, headings, and tables, then normalize them into chunks and build several indexes: a semantic index with embeddings for natural language queries; a symbol and usage index for functions, classes, types, and endpoints; a basic reference graph between files, symbols, and external docs; regex and file tree search for cases where you want deterministic matches over raw text.

When an agent calls Nia, it sends a natural language query plus optional hints like the current file path, stack trace, or repository. Nia runs a mix of BM25 style search, embedding similarity, and graph walks to rank relevant snippets, and can also return precise locations like “this function definition in this file and the three places it is used” instead of just a fuzzy paragraph. The calling agent then decides how to use those snippets in its own prompt. One Nia deployment can serve multiple agents and multiple projects at once. For example, you can have Cursor, Claude Code, and a browser based agent all pointed at the same Nia instance that knows about your monorepo, your internal wiki, and the provider docs you care about. We keep an agent agnostic session record that tracks which sources were used and which snippets the user accepted. Any MCP client can attach to that session id, fetch the current context, and extend it, so switching tools does not mean losing what has already been discovered.

A lot of work goes into keeping indexes fresh without reprocessing everything. Background workers periodically refetch configured sources, detect which files or pages changed, and reindex those incrementally. This matters because many of the worst “hallucinations” I have seen are actually the model quoting valid documentation for the wrong version. Fixing that is more about version and change tracking than about model quality.

We ship Nia with a growing set of pre-indexed public sources. Today this includes around 6k packages from common frameworks and provider docs, plus package search over thousands of libraries from ecosystems like PyPI, npm, and RubyGems, as well as pre indexed /explore page where everyone can contribute their sources! The idea is that a new user can install Nia, connect nothing, and still get useful answers for common libraries. Then, as soon as you add your own repos and internal docs, those private sources are merged into the same index. Some examples of how people use Nia so far: - migrating from one payments provider or API version to another by indexing the provider docs plus example repos and letting the agent propose and iterate on patches; - answering “how do I do X in this framework” by indexing the framework source directly instead of relying only on official docs that might be stale; - turning an unfamiliar public codebase into a temporary wiki to self onboard, where you can ask structural questions and jump to specific files, functions, or commits; - building a browser agent that answers questions using up to date code and docs even when the public documentation lags behind.

Nia is a paid product (https://www.trynia.ai/) but we have a free tier that should be enough for individuals to try it on real projects. Above that there is a self-serve paid plan for heavier individual use, and organization plans with higher limits, SOC 2, seat based billing, and options for teams that want to keep indexing inside their own environment. For private GitHub repos we can clone and index locally so code does not leave your infrastructure.

We store account details and basic telemetry like query counts and errors to operate the service, and we store processed representations of content you explicitly connect (chunks, metadata, embeddings, and small graphs) so we can answer queries. We do not train foundation models on customer content and we do not sell user data. Moreover, I can see Nia play out in the larger context of the agents space due to the global problem of providing reliable context to those systems. Early signals show that people are already using Nia for healthcare data, cloning Paul Graham by indexing all of his essays and turning him into an AI agent, using Naval’s archive to build a personalized agent, and more.

I would love to get Nia into the hands of more engineers who are already pushing coding agents hard and see where it breaks. I am especially interested in hearing about failure modes, annoying onboarding steps, places where the retrieval logic is obviously wrong or incomplete, or any security concerns I should address. I will be in the thread to answer questions, share more technical details, and collect any brutal feedback you are willing to give!

The article explores NVIDIA's 'virtuous cycle' strategy, which involves leveraging its GPU technology to drive growth in new markets and reinvest profits back into research and development, creating a self-reinforcing cycle of innovation and market expansion.

The article discusses the '10,000-hour rule' and the concept of deliberate practice, which suggest that achieving mastery in a field requires substantial time and effort. It explores the nuances and criticisms surrounding this idea, providing a balanced perspective on the role of practice and innate talent in skill development.

Legion Health (YC S21) operates a psychiatric practice and is building the AI-native operations layer for mental health care. We focus on the operational backend: scheduling, intake, documentation, billing, and care coordination. These workflows—not diagnostics—are the main bottlenecks in mental health delivery.

We run our own clinic, so the systems you build ship directly into real patient care. Our agent infrastructure currently supports more than 2,000 patients with one human support lead.

We’re hiring a Founding Engineer (in-person, San Francisco). You’d work directly with the founders on:

event-driven backend systems (Node.js, TypeScript, Postgres/Supabase, AWS)

LLM agent tooling (tool use, retries, memory, context management)

internal operations tools for both humans and agents

state/coordination logic that represents a patient’s journey

HIPAA-compliant data and audit pipelines

We’re open to backend or full-stack/product engineers who think in systems and have owned real workflows end-to-end. Prior experience with LLMs is optional; interest is required.

Details: full-time, in-person SF, salary $130k–$180k, equity 0.1–0.6%.

Apply here: https://www.ycombinator.com/companies/legion-health/jobs/oc6...

The article provides an in-depth analysis of the microcode structure of the 8086 processor, examining its instruction set, control flow, and microarchitectural details to gain a deeper understanding of the inner workings of this classic microprocessor.

Nova is a new programming language that aims to combine the simplicity and expressiveness of Python with the speed and performance of lower-level languages. It is designed to be a powerful and versatile language for a wide range of applications, from web development to data analysis and machine learning.

Delivery robots have been proliferating on Chicago's sidewalks, sparking debate and a petition calling for a pause on their deployment. The article discusses the growing presence of these autonomous delivery devices and the concerns raised by residents and officials about their impact on pedestrian safety and accessibility.

The article discusses the author's decision to cancel their O'Reilly media subscription due to concerns about the company's business practices and the value of the content offered. It highlights the author's reasons for the cancellation and their perspective on the overall quality and relevance of the O'Reilly subscription.

The article provides an overview of FoundationDB, a distributed database developed by Apple. It highlights FoundationDB's key features, including its ability to handle large-scale transactions, its fault tolerance, and its use of a custom programming language called Flow.