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The "modern" AI stack has a hidden performance problem: abstraction debt. We have spent the last two years wrapping LLMs in complex IDEs and orchestration frameworks, ostensibly for "developer experience". The research suggests this is a mistake. These wrappers truncate context to maintain low UI latency, effectively crippling the model's ability to perform deep, long-horizon reasoning & execution.

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The most performant architecture is surprisingly primitive: - raw Claude Code CLI usage - native Model Context Protocol (MCP) integrations - rigorous context engineering via `CLAUDE.md`

Why does this "naked" stack outperform?

First, Context Integrity. Native usage allows full access to the 200k+ token window without the artificial caps imposed by chat interfaces.

Second, Deterministic Orchestration. Instead of relying on autonomous agent loops that suffer from state rot, a "Plan -> Execute" workflow via CLI ensures you remain the deterministic gatekeeper of probabilistic generation.

Third, The Unix Philosophy. Through MCP, Claude becomes a composable pipe that can pull data directly from Sentry or Postgres, rather than relying on brittle copy-paste workflows.

If you are building AI pipelines, stop looking for a better framework. The alpha is in the metal. Treat `CLAUDE.md` as your kernel, use MCP as your bus, and let the model breathe. Simplicity is the only leverage that scales.

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To operationalize this, we must look at the specific primitives Claude Code offers that most developers ignore.

Consider Claude Hooks These aren't just event listeners; they are the immune system of your codebase. By configuring a `PreToolUse` hook that blocks git commits unless a specific test suite passes, you effectively create a hybrid runtime where probabilistic code generation is bounded by deterministic logic. You aren't just hoping the AI writes good code; you are mathematically preventing it from committing bad code.

Then there is the Subagentic Architecture In the wrapper-world, subagents are opaque black boxes. In the native CLI, a subagent is just a child process with a dedicated context window. You can spawn a "Researcher" agent via the `Task` tool to read 50 documentation files and return a summary, keeping your main context window pristine. This manual context sharding is the key to maintaining "IQ" over long sessions.

Finally, `settings.json` and `CLAUDE.md` act as the System Kernel While `CLAUDE.md` handles the "software" (style, architectural patterns, negative constraints), `settings.json` handles the "hardware" (permissions, allowed tools, API limits). By fine-tuning permissions and approved tools, you create a sandbox that is both safe and aggressively autonomous.

The future isn't about better chat interfaces. It's about "Context Engineering" designing the information architecture that surrounds the model. We are leaving the era of the Integrated Development Environment (IDE) and entering the era of the Intelligent Context Environment.

I’m part of the Developer Crew at io.net, where we’re working on decentralized compute for AI workloads and agent-based systems.

My focus here isn’t promotion, but sharing practical learnings from the builder side things like how AI agents behave when compute is modular, what breaks in real usage, and how developers actually think about decentralized infra in production.

I’m here to learn from the community, contribute where I can, and exchange notes with others building or experimenting in this space.

Happy to answer questions or dig into specifics if useful.

First off, this thread is NOT a petition to rally against the moderation team. Considering the deluge of trash they deal with every day, I think they are doing a valiant job and are to be commended. Consider it merely a place to discuss, which is what HN does best.

That said, it's becoming more and more obvious every day that there is a tremendous amount of attempts by bots, and specifically AI agents, to inject slop into HN threads. I worry about the integrity of the discourse here and if the ever growing wave of garbage will overtake staff resources to deal with it. Is it time to implement captcha for HN? If so, should it be out of the box, or a new mechanism more tailored to the security and privacy-centric nature of the HN readership? Are captchas even still effective enough in the age of AI to warrant their use?

The best phone in 2026 is a dying iPhone 12 mini. It’s the smallest smartphone still maintained by a major manufacturer (both software updates and repair parts), runs all apps securely & as designed, costs ~$150 with accessories available for dirt cheap (my favorite sleeve officially made by Apple [1] cost $129 on release but is now available for around $10). A dying battery makes the phone better - it forces you use the phone less and only when you really need it. Don’t replace your battery, just turn the phone off and you’ll feel immediately less stressed. If you don’t want to miss calls, get an old Apple Watch with cellular and use it as your main “phone”. Buy a dying iPhone mini to save the planet and yourself, and maybe if Apple sees enough active minis they will make another one...

[1]: https://apple.fandom.com/wiki/IPhone_12_Leather_Sleeve_with_MagSafe

Why are they behaving like this since last year (trying very hard to burn themselves to the ground)

Latest example:

Microsoft Office renamed to “Microsoft 365 Copilot app” (https://news.ycombinator.com/item?id=46496465)

Not only the rename is absurd, but the page (office.com) looks heavily vibe-coded

Share your information if you are looking for work. Please use this format:

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Readers: please only email these addresses to discuss work opportunities.

There's a site for searching these posts at https://www.wantstobehired.com.

I started building Super Productivity in late 2016 because I needed to log time against my Jira tickets. Ironically, I've never had to do that again on any project since.

But I kept building it anyway and for some reason I couldn't stop doing it. 8 years later it's a local-first task manager with time tracking and integrations for Jira, GitHub, GitLab, and others. Everything runs on your device - no cloud, no account required.

Why local-first? Three reasons: - I didn't want to run servers or deal with auth systems - I care about not having my work habits tracked - I needed something that works offline - Most of the companies I worked for would not allow for putting that kind of data into a random cloud service

Biggest lesson from 8 years: saying no is sometimes harder than building features. Every "quick addition" someone requests has hidden complexity and long term costs. I know how much effort goes into drafting ideas, so I often had a very hard time saying no to new additions, especially if people already provided the code and even more if it was good clean and tested code.

Now there is a plugin system with community plugins and this makes it much easier.

Still figuring out: sustainable funding without ads or selling data. Currently, it's donations + my own time. Would love to hear how others approach this.

Repo: https://github.com/johannesjo/super-productivity Try it: https://super-productivity.com

I often have 5-10m gaps. It’s too easy to waste this time.

What things do you like to do in these increments?

For instance, learning a new skill, getting slightly better at something, reading high quality content.

Edited to clarify that I don’t mean phone-specific activities!

Please state the location and include REMOTE for remote work, REMOTE (US) or similar if the country is restricted, and ONSITE when remote work is not an option.

Please only post if you personally are part of the hiring company—no recruiting firms or job boards. One post per company. If it isn't a household name, explain what your company does.

Please only post if you are actively filling a position and are committed to responding to applicants.

Commenters: please don't reply to job posts to complain about something. It's off topic here.

Readers: please only email if you are personally interested in the job.

Searchers: try https://dheerajck.github.io/hnwhoishiring/, http://nchelluri.github.io/hnjobs/, https://hnresumetojobs.com, https://hnhired.fly.dev, https://kennytilton.github.io/whoishiring/, https://hnjobs.emilburzo.com, or this (unofficial) Chrome extension: https://chromewebstore.google.com/detail/hn-hiring-pro/mpfal....

Don't miss this other fine thread: Who wants to be hired? https://news.ycombinator.com/item?id=46466073

## My Implementation

I'm using a 64-bit layout:

- Bits 63-51: Quiet NaN signature (0x7FFC...)

- Bits 50-18: 32-bit payload (integers, string pool indices, etc.)

- Bits 17-3: Unused/ (15 bits)

- Bits 2-0: 3-bit type tag

So it allows me to have 5 tagged types: `TRUE_VAL`, `FALSE_VAL`, `STRING_VAL`, `CALLDATA_VAL`, `U32_VAL`

This is for a domain-specific VM I'm building for programmable task management (think "Vim for todo apps" - small core with scriptable behaviors). The VM is stack-based with:

- String pooling & instructions pooling (indices stored as NaN-boxed values) - Call stack for task instructions execution.

code is here : https://github.com/tracyspacy/spacydo/blob/main/src/values.rs

I have a decade of mixed experience: 10+ years in IT, 2 years on a professional motorsports team (Tony Kart, Ducati, Miata), and 4 years in software (refactoring old .NET to Spring Boot/Angular and some greenfield projects). I self-taught my way into dev and finished a CS degree while working.

I’m currently at a {big_slow_corp} and feeling the AI squeeze trying to switch jobs. My exp is not public facing so the “experience building scalable apps to disrupt the market” requirement I think is my weakness. I’ve also realized my heart isn’t in web frameworks. I’m the guy bored on a plane talking to his gf about flap software, throttle response, and suspension geometry like a kid showing a toy to his mom.

I want to move "closer to the metal", ECUs, controlling machinery, I’d love ML applied to machinery, even infotainments!, but my professional resume is strictly high-level. My tinkering experience includes building MegaSquirts and using ESP32s for signal filtering and logic, data loggers and wiring things you’d find in a race car, but I currently lack a project car to demonstrate new work.

How can I bridge the gap from Java/Angular to automotive software?

Does a decade of IT and 4 in backend experience carry weight in the "Software Defined Vehicle" world, or am I starting at zero?

What "proof of work" can I build to wow recruiters at places like Toyota Research or Tesla (any brand with US presence really) without a physical car to hack on?

I'm ready to pivot to where my passion actually is. Any advice is appreciated.

Dear Americans, please don’t take this the wrong way - I love the US, have friends there, and treasure memories I made there.

However, it seems plausible that the US is turning into a rogue, authoritarian, Russia-like state increasingly more friendly towards Russia and hostile towards Europe. I am a European who grew up in a country still occupied by Russia. I am increasingly more worried about building my projects on American platforms, using an American operating system, etc.

What if the US actually attacks Greenland or finds another way to be openly hostile to Europe? I am not saying it will happen. All I am saying is that it seems prudent to prepare. How would you do it?

It is currently impossible to unhook myself from the US, but I would like to minimize exposure.

I can’t do anything about things like building an alternative to VISA/MasterCard (except wait for the digital Euro), so I will focus on things I can actually do and ignore things like my government buying F-35s and possibly giving my health data to Palantir.

* Mobile phone - there are no real European alternatives; it’s just Apple vs Google. Samsung or HTC with Android seems like a less bad option.

* Operating system - I have been using Linux for ages, getting rid of Windows seems relatively easy.

* Social networks - I grew to hate them before the current US admin, never used TikTok or Instagram, and I mostly stopped using Facebook and Twitter around the time Musk bought Twitter.

* Stripe for payments - this will be hard, but I am experimenting with our local payment processor, and so far it seems surprisingly doable, but it is not a battle-tested solution like Stripe.

* Clerk authentication - doable, but a lot of work and worrying

* AWS - I had a surprisingly bad experience with AWS and switched to a local provider with a lot less functionality (that I mostly do not need) and a lot better support

* GitHub, Cloudflare... dear God, how could we Europeans allow ourselves to be that dependent on anyone? Everything I touch is American.

* Gmail - this will be hard (two decades of emails). Any advice?

* Anything AI-related - fuuu, I am lost here.

What am I missing/forgetting? What do/would you do in my place?

I really hope you will take this as a brainstorming exercise and not an attack on America. I really do love the US and hope its democracy turns out to be more resilient than it currently seems.

EDIT: Please kindly keep responses practical. Let’s not turn this into a political discussion. You might approach it as a “what if” exercise, even if you think what the current US admin is doing is great, Europeans deserve what they get, etc.

I’m researching patterns for edge / gateway telemetry where the network is unreliable (remote sites, industrial, fleets, etc.) and you need offline buffering + bounded disk + replay once connectivity returns.

Questions for folks running this in production:

What do you use today? (MQTT broker + ??, Kafka/Redpanda/NATS, Redis Streams, custom log files, embedded DB, etc.)

Where do you buffer during outages: append-only log, SQLite/RocksDB, queue-on-disk, something else?

How do you handle backpressure when disk is near full? (drop policy, compression, sampling, prioritization)

What’s your failure nightmare: corruption, replay storms, duplicates, “stuck” consumer offsets, disk-full, clock skew?

What guarantees do you actually need: zero-loss vs “best effort” (and where do you draw that line)?

What metrics/alerts matter most on gateways? (queue depth, replay rate, oldest event age, fsync latency, disk usage, etc.)

I’d love to learn what works, what breaks, and what you wish existing tools did better.

All you need to do is to call it with @rho and tell it which environment you want to deploy to. GCP cloud run is currently supported.

I've seen a few anecdotes lately that say that they use Claude Code on legacy codebase and with relatively little supervision it can work on complex problems. Then the claim that Claude Code writes most of its own code, and that they no longer mentor their newcomers - instead, AI answers their questions and they can start making meaningful changes within the first few days. To me it sounds almost too good to be true, so I'd love to have some reality check.

I've spent most of my career in legacy codebases, reading, tracing behavior, making careful changes, and writing tests to protect them. I've taken a sabbatical though, which ends soon, and I'm quite worried and excited to what has happened during this time.

For those working on legacy codebases:

- Has the workflow really shifted to prompting AI, reviewing output, and maintaining .md instructions?

- Does your company allow Claude Code, Codex or similar tools? If not, what do you use?

- Do companies worry about costs and code privacy?

- Where do you think this is headed, a year from now?

Concrete examples, good or bad, would be especially helpful. Thanks.

If you or your organization are developing projects that advance amateur radio or digital communications, now’s a great time to apply for ARDC grant funding, with our next application deadline on February 1, 2026.

ARDC’s priority areas for funding reflect our broader vision and strategy for supporting experimentation, education, and open technology within the amateur radio and digital communications communities. We’re especially interested in projects that align with these three areas:

Research & Development (R&D): open hardware and software systems that enable learning and experimentation (e.g. SDRs, open codec technologies, new modulation techniques).

Space-Based Communications: projects that create or expand access to satellite communications for amateur radio (AR) and digital communications (DC), engaging communities in wireless experimentation (e.g. GEO or HEO programs, repurposed commercial satellites, space-based tools for learning).

Open Source Education: scalable, open educational materials and hands-on projects that make AR and DC more accessible, especially for new learners and clubs (e.g. curricula, videos).

While we welcome proposals across the full range of AR and DC, projects that align with these areas remain a priority in our grantmaking decisions.

Learn more about these priority areas at https://www.ardc.net/apply/priority-areas-for-funding/, and find information on eligibility and how to apply at https://www.ardc.net/apply/. For additional questions, contact giving@ardc.net.

RevisionDojo is a YC-backed test prep company ($3.4M raised) that sells International Baccalaureate (IB) test prep. Over the past year, users on r/IBO sub-reddit have documented a pattern of unethical marketing practices:

*Astroturfing:* Coordinated campaigns where accounts pose as students sharing "cheatsheets" and "predicted exam leaks." Other accounts then upvote, leave supportive comments, and ask follow-up questions—creating the illusion of organic student excitement. Multiple threads have exposed this pattern [1][2][3].

*Paid fake posts:* High school students report being offered payment to write promotional Reddit posts [4].

*Pressuring critics:* Users who post negative reviews report being contacted directly by company representatives, told it's "a shame" they're posting publicly [5]. Critical comments receive coordinated mass downvotes [6].

*Soliciting copyrighted materials:* They use TikTok influencers and fake reddit posts to persuade students to sell them official IB exam papers, violating IB policies [7].

The r/IBO moderators are actively investigating [8].

These practices appear to be working great for them. Recently, they acquired OnePrep (oneprep.xyz), a free SAT prep tool that was already popular on r/sat. Since the acquisition, the same manipulation tactics have been deployed at scale: 150 Trustpilot reviews in a window of a few days [9], and widespread coordinated Reddit manipulation—multiple accounts posting "tips" that recommend Oneprep, coordinated upvoting, and fake enthusiasm in comments. The most prominent example was a 2,000+ upvote post removed by moderators for manipulation, but it's part of a sustained campaign across the subreddit.

*Sources:*

[1] https://www.reddit.com/r/IBO/comments/1p55qun/ [2] https://www.reddit.com/r/IBO/comments/1jsb00a/ [3] https://www.reddit.com/r/IBO/comments/1ohcohi/ [4] https://www.reddit.com/r/IBO/comments/1p55qun/comment/nqmhal3/ [5] https://www.reddit.com/r/IBO/comments/1my1ajx/comment/na94upv/ [6] https://www.reddit.com/r/IBO/comments/1my1ajx/comment/na8zvs4/ [7] https://www.reddit.com/r/IBO/comments/1mej900/ [8] https://www.reddit.com/r/IBO/comments/1my1ajx/comment/nagdkl5/ [9] https://www.trustpilot.com/review/oneprep.xyz

Hi everyone I saw zig is and intresting language I am learning it and also making a transpilied High level language over it I want some help with developing syntax. in my lang there are three types of var declaration 1) using local keyword this are added in arena of the specific function. 2) using let keyword this are on stack but i am finding solution to make strings easier here. 3) manual memory but my transpiler will automatically use defer keywords so they are safe and delete once block exit 4) using unsafe direct fully manual memory management but still my transpiler will not let compiler till once in code the are freed.

If I'm writing a new lightweight application that requires LLM-based text completion to power a feature, is there a standard way to request the user's operating system to provide a completion?

For instance, imagine I'm writing a small TUI that allows you to browse jsonl files, and want to create a feature to enable natural language parsing. Is there an emerging standard for an implementation agnostic, "Translate this natural query to jq {natlang-query}: response here: "?

If we don't have this yet, what would it take to get this built and broadly available?

I'm looking for some books to help me practise and refine my skills as a developer and Engineer.

I'm currently working in python on a django project, working in a financial domain. I lead a few engineers and direct/manage some projects. But I feel like I'm missing out on something when I read about people making things in zig and rust, or how they apply some numerical modelling techniques to certain problems, plus the new technologies being developed. I feel like I'm very much not knowledgeable or distinguishable enough, so I want to refine my skill a bit and maintain "sharp" in case of something happening and i need to find a new job quickly. And i want to make sure that I'm learning all that I could be learning in my current position.

Some previous books I've read / enjoyed:

* The makings of an Expert Engineer * Designing data intensive applications (haven't finished, moved house and lost the book, want to pick it up again) * Designing Elixir Systems with OTP * Practical Common Lisp

I feel like I have learned a bit with the Elixir/CL books, inwhich I apply to how i write python, but I never branch out to doing my own projects in these languages, so I feel like I'm missing out on utilizing these tools fully.

Is there anything to read that could take me to the proverbial next level?

Hi HN,

We are working on a document vault designed for people and organizations who cannot accept cloud exposure.

The system is intentionally boring in some ways: • No required accounts • No cloud dependency for core functionality • Fully offline operation • Local encryption • Air-gapped storage • Encrypted export and controlled, encrypted printing

The printing piece is why we started this. In many environments, printing is still unavoidable, and it remains one of the largest data-leak vectors. Most privacy tools stop at storage and ignore output entirely.

This is not meant to replace cloud storage for everyone. It is for cases where the threat model assumes: • Networks are hostile • Cloud accounts will eventually be compromised • Convenience must sometimes be traded for control

We are explicitly not claiming: • “Unhackable” • “Military-grade” • “Zero risk”

We are trying to minimize attack surface and failure modes, not eliminate them.

We would genuinely value feedback on: • Threat model blind spots • Encrypted printing assumptions • Physical access risks • Update and key management strategies • What would make you immediately distrust this

If this sounds like something you would never use, that is also useful feedback.

Thanks.