Hacker News

We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention is the right primitive for this, and FFN parameters are wasted at this scale.

Simple Attention Networks: the entire model is just attention and gating, no MLPs anywhere. Needle is an experimental run for single-shot function calling for consumer devices (phones, watches, glasses...).

Training: - Pretrained on 200B tokens across 16 TPU v6e (27 hours) - Post-trained on 2B tokens of synthesized function-calling data (45 minutes) - Dataset synthesized via Gemini with 15 tool categories (timers, messaging, navigation, smart home, etc.)

You can test it right now and finetune on your Mac/PC: https://github.com/cactus-compute/needle

The full writeup on the architecture is here: https://github.com/cactus-compute/needle/blob/main/docs/simp...

We found that the "no FFN" finding generalizes beyond function calling to any task where the model has access to external structured knowledge (RAG, tool use, retrieval-augmented generation). The model doesn't need to memorize facts in FFN weights if the facts are provided in the input. Experimental results to published.

While it beats FunctionGemma-270M, Qwen-0.6B, Granite-350M, LFM2.5-350M on single-shot function calling, those models have more scope/capacity and excel in conversational settings. We encourage you to test on your own tools via the playground and finetune accordingly.

This is part of our broader work on Cactus (https://github.com/cactus-compute/cactus), an inference engine built from scratch for mobile, wearables and custom hardware. We wrote about Cactus here previously: https://news.ycombinator.com/item?id=44524544

Everything is MIT licensed. Weights: https://huggingface.co/Cactus-Compute/needle GitHub: https://github.com/cactus-compute/needle

https://www.reddit.com/r/Android/comments/1tb8xls/introducin...

https://www.hypercubic.ai/), bringing AI tools to the mainframe and COBOL world. (We did a Launch HN last year: https://news.ycombinator.com/item?id=45877517.) Today we’re launching Hopper, an agentic development environment for mainframes.

You can download it here: https://www.hypercubic.ai/hopper, and you can also request access and immediately get a mainframe user account to play with.

There's also a video runthrough at https://www.youtube.com/watch?v=q81L5DcfBvE.

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/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/SYSPRINT output, interpreting condition codes, patching fixed-width source, and resubmitting.

Much of this work is so well-defined and repetitive that it'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/OS surfaces.

For example, here is a tiny version of the kind of thing Hopper can help debug:

  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 "UPDATED BALANCE: " CUSTOMER-BALANCE
       STOP RUN.


  JCL:

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

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'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.

https://gigacatalyst.com/). Gigacatalyst allows sales, CS, and users to build one-off features, so your SaaS can support long-tail customer workflows and engineers aren’t pulled away from the roadmap.

When you sell software to large businesses, you realize that each customer needs their own workflow and features. Traditionally, this either means long engineering roadmaps or the customers end up using workarounds.

But what if everyone could build their critical missing features just by talking to an AI? That’s what we do at Gigacatalyst. We provide an AI customization layer for your customers, CS team, and sales team to build these missing critical workflows without needing any engineers at all. Think Lovable, but built on top of YOUR platform.

We connect to your product's APIs, learn your data model and design system, and let non-technical users build governed apps via natural language - inside your product, under your brand.

Here’s what it looks like in action: https://www.youtube.com/watch?v=_taSpSphH6E

One of our customers, a Series B company, saw their users (not engineers - managers, ops people, facility directors) build critical workflows like:

- Parts stockout prevention: A maintenance manager typed "show me which parts will run out in the next 2 weeks based on usage over the last 90 days, accounting for vendor lead times." The app tracks consumption velocity, forecasts stockouts, and alerts before it's too late. He says it's prevented ~$500K in emergency downtime.

- Invoice OCR from phone photos: Technicians kept losing paper invoices. The prompt: "upload a photo of the invoice, extract vendor name, date, amount, and line items, then match it to the purchase order and flag discrepancies." Now techs snap a photo on-site to automatically add to the system of record.

- Restaurant emergency triage: A pizza chain's facilities manager was drowning in maintenance requests. He built a priority matrix: "walk-in freezer not cooling" auto-routes as CRITICAL, "dining room light flickering" goes to LOW. He's now able to manage backlogs with the correct priority.

How Gigacatalyst works under the hood:

1. Agentic API discovery: Our agents go through your app and parse your endpoints, query params, request/response shapes, and sample data to build the base layer.

2. Generation and Validation: When a user describes what they want our AI generates an app. We set up multiple validation steps, including static checks, runtime error analysis, and LLM-as-a-judge.

3. Sandboxing and Compilation: We wrote our own compilation and sandboxing framework to get the fastest speeds and lowest costs. This means that users can interact with the built app in seconds.

4. Proxy layer: We create a proxy layer for all APIs to handle auth, tenant isolation, and rate limiting. Everything the agent has access to is controlled, logged, observed, and version controlled.

After 2000+ daily users, 900+ apps built, and 70% 30-day retention, today we're opening a public demo.

Try it: https://app.gigacatalyst.com/ - enter your SaaS product's API URL (or just the homepage) and start prompting.

If you're serving a variety of use cases, you probably deal with a lot of custom requests and Gigacatalyst will save you time and increase your bottom line. Book a meeting at https://gigacatalyst.com/#contact and I'll help your team and customers build new functionality on top of your platform.

I've been reading Hacker News since I was 12 years old. I'm proud to launch for all of you and I want to hear your feedback on my product and comments!

https://voker.ai/), an agent analytics platform for AI product teams. Voker gives full visibility into what users are asking of your agents, and whether your agents are delivering, without having to dig through logs. Our main product is a lightweight SDK that is LLM stack agnostic and purpose-built for agent products. (https://app.voker.ai/docs)

Agent Engineers and AI product teams don’t have the right level of visibility into agent performance in production, which results in bad user experiences, churn, and hundreds of hours wasted with spot checks to find and debug issues with agent configurations.

Demo: https://www.tella.tv/video/vid_cmoukcsk1000i07jgb4j65u67/vie...

We recently conducted a survey of YC Founders and 90%+ of respondents said that the only way they know if their Agents are failing users in production is by hearing complaints from customers. They push a prompt change hoping that it fixes the problem and doesn’t break something somewhere else, and the cycle repeats.

We saw tons of observability and evals products popping up to try to address these problems, but we still felt like something was missing in the agent monitoring stack. Obs is good for individual trace debugging but is only accessible to engineers. Evals are good for testing known issues, but don't give insights into trends that teams don’t expect, so engineers are always playing catch up. Traditional product analytics tools do a good job tracking clicks and pageviews across your product surface but weren’t built ground up for agent products. Knowing what users want out of agents, and whether the agent delivered requires specific conversational intelligence / unstructured data processing techniques.

We came up with the agent analytics primitives of Intents, Corrections, and Resolutions to describe something pretty much all conversational agents had in common: a user will always come to an agent with an intent, the user might have to correct this agent on the way to getting their intent resolved, and hopefully every intent a user has is eventually resolved by the agent. Voker processes LLM calls by automatically annotating individual conversations and picking out user intent and corrections. Voker takes these and uses LLMs and hierarchical text classification to create dynamic categories that give higher level insights so you don’t have to read individual conversations to know what are the main usage patterns across your users.

The most common substitute solution we’ve seen is uploading obs logs to Claude or ChatGPT and asking for summary insights. There are a few problems with this - mainly that LLMs aren’t good at math or data science, so you don’t get accurate or consistent statistics. Its highly likely that the LLM overfits to some insights and underfits to others. The LLM isn’t programmatically reading and classifying each individual session or interaction. This is why we don’t use LLMs for any of our core data engineering (processing events, calculating statistics) so the analytics we produce are consistent, reproducible, and accurate. We have a publicly available, lightweight SDK that wraps LLM calls to OpenAI, Anthropic and Gemini in Python and Typescript. Voker handles the data engineering to turn raw data into usable analytics primitives and higher level insights. Free tier: 2,000 events / mo, requires email signup. Paid plans start at $80/mo with a 30 day free trial.

We'd love to hear how you're currently detecting trends, and if you try Voker, tell us what part of our analysis is valuable, and what still feels missing. Thanks for reading, and we’re looking forward to your thoughts in the comments!

I'm Ben Cochran, I spent 20+ years in the trenches with full-stack Engineering, DevOps, high performance computing & 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't retain enough context to produce accurate edits. More on the research bit: https://statewright.ai/research

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'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's attempting to do something that isn'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'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:

/plugin marketplace add statewright/statewright

/plugin install statewright

/reload-plugins

Then "start the bugfix workflow" or /statewright start bugfix. You'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'd want to build first. Agents are suggestions, states are laws.

https://www.instructure.com/incident_update#:~:text=STATUS%2...

https://www.nytimes.com/2026/05/12/us/canvas-instructure-hac..., https://archive.ph/HIkdn

https://github.com/TanStack/router/issues/7383