I moved my digital stack to Europe

MacBook Neo Deep Dive: Benchmarks, Wafer Economics, and the 8GB Gamble

The conflation of money and things

Related: Google Cloud fraud defense, the next evolution of reCAPTCHA - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48039362">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48039362</a>also: Google Cloud Fraud Defence is just WEI repackaged - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48063199">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48063199</a>

Google broke reCAPTCHA for de-googled Android users

Computer Hobby Movement in Canada

My graduation cap runs Rust

If AI writes your code, why use Python?

Show HN: Sx – an open-source package manager for AI skills, MCPs, and commands

The limits of Rust, or why you should probably not follow Amazon and Cloudflare

Software Internals Book Club

AI is making me dumb

An attempt at a single pass LLVM frontend in ~3000 lines of C without external dependencies, malloc, or an AST. Included are some graphical examples. The IR isn&#x27;t perfect, and the README touches on one particular downfall

Show HN: Nibble

The Tree House: A voyage to the source of a backyard dream

delta time

What the Hell Was Going on with Cigarette Ads in the 70s? (2024)

Training an LLM in Swift, Part 1: Taking matrix mult from Gflop/s to Tflop/s

<a href="https:&#x2F;&#x2F;www.sciencedirect.com&#x2F;science&#x2F;article&#x2F;abs&#x2F;pii&#x2F;S1369702123002390?via%3Dihub" rel="nofollow">https:&#x2F;&#x2F;www.sciencedirect.com&#x2F;science&#x2F;article&#x2F;abs&#x2F;pii&#x2F;S13697...</a>

New stainless steel can survive conditions for hydrogen production in seawater

Int a = 5; a = a++ + ++a; a =? (2011)

When life gives you lemons, write better error messages

Extremely Low Frequencies

Power Tools Got Worse on Purpose. Who Owns DeWalt, Craftsman, and Milwaukee?

Hi HN, we’re Sai and Aayush, and we’re building Hypercubic (<a href="https:&#x2F;&#x2F;www.hypercubic.ai&#x2F;">https:&#x2F;&#x2F;www.hypercubic.ai&#x2F;</a>), bringing AI tools to the mainframe and COBOL world. (We did a Launch HN last year: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=45877517">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=45877517</a>.) Today we’re launching Hopper, an agentic development environment for mainframes.You can download it here: <a href="https:&#x2F;&#x2F;www.hypercubic.ai&#x2F;hopper">https:&#x2F;&#x2F;www.hypercubic.ai&#x2F;hopper</a>, and you can also request access and immediately get a mainframe user account to play with.There&#x27;s also a video runthrough at <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=q81L5DcfBvE" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=q81L5DcfBvE</a>.Mainframes still run a surprising amount of critical infrastructure: banking, payments, insurance, airlines, government programs, logistics, and core operations at large institutions. Many of these systems are decades old, but they continue to process enormous transaction volumes because they are reliable, secure, and deeply embedded into business operations.A lot of that software is written in COBOL and runs on IBM z&#x2F;OS. The development environment looks very different from modern cloud or Unix-style development. Instead of GitHub, shell commands, package managers, and CI pipelines, developers often work through TN3270 terminal sessions, ISPF panels, partitioned datasets, JCL, JES queues, spool output, return codes, VSAM files, CICS transactions, and shop-specific conventions.TN3270 is the terminal interface used to interact with many IBM mainframe systems. ISPF is the menu and panel system developers use inside that terminal to browse datasets, edit source, submit jobs, and inspect output. It is powerful and reliable, but it was designed for expert humans navigating screens, function keys, and fixed-width workflows, not AI agents.A simple COBOL change might require finding the right source member, checking copybooks, locating compile JCL, submitting a job, reading JES&#x2F;SYSPRINT output, interpreting condition codes, patching fixed-width source, and resubmitting.Much of this work is so well-defined and repetitive that it&#x27;s a good fit for agentic AI. To get that working, however, a chatbot next to a terminal is not enough. The agent needs to operate inside the mainframe environment.Hopper combines three things: (1) A real TN3270 terminal, (2) Mainframe-aware panels for datasets, members, jobs, and spool output, and (3) An AI agent that can operate across those z&#x2F;OS surfaces.For example, here is a tiny version of the kind of thing Hopper can help debug:<pre><code> COBOL:

 IDENTIFICATION DIVISION.
 PROGRAM-ID. PAYCALC.

 DATA DIVISION.
 WORKING-STORAGE SECTION.
 01 CUSTOMER-BALANCE PIC 9(7)V99.

 PROCEDURE DIVISION.
 ADD 100.00 TO CUSTOMER-BALNCE
 DISPLAY &quot;UPDATED BALANCE: &quot; CUSTOMER-BALANCE
 STOP RUN.


 JCL:

 &#x2F;&#x2F;PAYCOMP JOB (ACCT),&#x27;COMPILE&#x27;,CLASS=A,MSGCLASS=X
 
 &#x2F;&#x2F;COBOL EXEC IGYWCL
 
 [&#x2F;&#x2F;COBOL.SYSIN](https:&#x2F;&#x2F;cobol.sysin&#x2F;) DD DSN=USER1.APP.COBOL(PAYCALC),DISP=SHR
 
 [&#x2F;&#x2F;LKED.SYSLMOD](https:&#x2F;&#x2F;lked.syslmod&#x2F;) DD DSN=USER1.APP.LOAD(PAYCALC),DISP=SHR

</code></pre>
A human would submit this job, inspect JES output, open `SYSPRINT`, find the undefined `CUSTOMER-BALNCE`, map it back to the source, patch the member, and resubmit. Hopper is designed to let an agent operate through that same loop autonomously.Hopper is not trying to hide the mainframe behind a generic abstraction, and it&#x27;s not a chatbot. The design principle is simple: preserve the fidelity of the mainframe environment, but make it accessible to AI agents.Sensitive operations require approval, and the terminal remains visible at all times.Once agents can operate inside the mainframe environment, new workflows become possible: faster job debugging, automated documentation, safer code changes, test generation, migration planning, traffic replay, and modernization verification.We’re curious to hear your thoughts! especially from anyone who has worked with mainframes, COBOL or has done legacy enterprise modernization.

Show HN: Agentic interface for mainframes and COBOL

Gmail registration now requires scanning a QR code and sending a text message

How to make your text look futuristic (2016)

Australia orders China-linked investors to sell Northern Minerals stake

Fc, a lossless compressor for floating-point streams

Screenshots of Old Desktop OSes

Agentic problem solving in its current state is very brittle. I fell in love with it, but it creates as many problems as it solves.I&#x27;m Ben Cochran, I spent 20+ years in the trenches with full-stack Engineering, DevOps, high performance computing &amp; ML with stints at NVIDIA, AMD and various other organizations most recently as a Distinguished Engineer.For agents to work reliably you either need massive parameter counts or massive context windows to keep the solution spaces workable. Most people are brute forcing reliability with bigger models and longer prompts.What if I made the problem smaller instead of making the model bigger?I took a different approach by using smaller models: models in the 13-20B parameter range and set them to task solving real SWE-bench problems. I constrained the tool and solution spaces using formal state machines. Each state in the machine defines which tools the model can access, how many iterations it gets and what transitions are valid. A planning state gets read-only tools. An implementation state gets edit tools (scoped to prevent mega edits) and write friendly bash tools. The testing state gets bash but only for testing commands. The model cannot physically skip steps or use the wrong tool at the wrong time. It is enforced via protocol, not via prompts.The results were more promising than I would have expected. Across multiple model families irrespective of age (qwen-coder, gpt-oss, gemma4) and the improvements were consistent above the 13B parameter inflection point. Below that, models can navigate the state machine but can&#x27;t retain enough context to produce accurate edits. More on the research bit: <a href="https:&#x2F;&#x2F;statewright.ai&#x2F;research" rel="nofollow">https:&#x2F;&#x2F;statewright.ai&#x2F;research</a>Surprisingly this yielded improvements in frontier models as well. Haiku and Sonnet start to punch above their weight and Opus solves more reliably with fewer tokens and death spirals. Fine tuning did not yield these kinds of functional improvements for me. The takeaway it seems is that context window utilization matters more than raw context size - a tightly scoped working context at each step outperforms a model given carte blanche over everything. Constraining LLMs which are non-idempotent by using deterministic code is a pattern that nobody is currently talking about.So, I built Statewright. Its core is a Rust engine that evaluates state machine definitions: states, transitions, guards and tool restrictions. Its orchestration doesn&#x27;t use an LLM, just enforces the state machine. On top of that is a plugin layer that integrates with Claude Code (and soon Codex, Cursor and others) via MCP. When you activate a workflow, hooks enforce the guardrails per state automatically. The model sees 5 tools available instead of dozens, gets clear instructions for the current phase and transitions when conditions are met. Importantly it tells the model when it&#x27;s attempting to do something that isn&#x27;t in scope, incorrect or when it needs to try something else after getting stuck.You can use your agent via MCP to build a state machine for you to solve a problem in your current context. The visual editor at statewright.ai lets you tweak these workflows in a graph view... You can clearly see the failure paths, the retry loops and the approval gates. State machines aren&#x27;t DAGs; they loop and retry, which is what agentic work actually needs.Statewright is currently live with a free tier, try it out in Claude Code by running the following:&#x2F;plugin marketplace add statewright&#x2F;statewright&#x2F;plugin install statewright&#x2F;reload-pluginsThen &quot;start the bugfix workflow&quot; or &#x2F;statewright start bugfix. You&#x27;ll need to paste your API key when prompted. The latest versions of Claude may complain -- paste the API key again and say you really mean it, Claude is just being cautious here.Feedback is welcome on the workflow editor, the plugin experience, and tell me what workflows you&#x27;d want to build first. Agents are suggestions, states are laws.

Show HN: Statewright – Visual state machines that make AI agents reliable

Of course I don&#x27;t mean they stopped being fun for everyone. My impression is that they&#x27;ve been on one side &quot;corporatized&quot;, and on the other became a vehicle for mindless entertainment.I don&#x27;t care for coding new stuff. Everything I may need either already exists or is too complex to do on my own (and no, I won&#x27;t vibe-code it, what&#x27;s the fun in that?)I don&#x27;t even code for work anymore since I moved to a project&#x2F;service management role.Basically, the spark I felt some 25 years ago seems to be completely gone.Any suggestion on getting it back?

Ask HN: When did computers stop being fun?

Leaving GitHub for Forgejo