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Show HN: Nibble

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't perfect, and the README touches on one particular downfall

Show HN: Nibble

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't perfect, and the README touches on one particular downfall

Show HN: Nibble

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't perfect, and the README touches on one particular downfall

Show HN: Running the second public ODoH relay

Every privacy-focused DNS service requires an account: NextDNS, Cloudflare for Families, Apple's iCloud Private Relay (paid, iOS-only). The protocol that doesn’t require one - ODoH - had basically one well-known public relay operator (Frank Denis on Fastly Compute, default in dnscrypt-proxy). I built a second one and the client to talk to it.

Show HN: Running the second public ODoH relay

Every privacy-focused DNS service requires an account: NextDNS, Cloudflare for Families, Apple's iCloud Private Relay (paid, iOS-only). The protocol that doesn’t require one - ODoH - had basically one well-known public relay operator (Frank Denis on Fastly Compute, default in dnscrypt-proxy). I built a second one and the client to talk to it.

Show HN: Running the second public ODoH relay

Every privacy-focused DNS service requires an account: NextDNS, Cloudflare for Families, Apple's iCloud Private Relay (paid, iOS-only). The protocol that doesn’t require one - ODoH - had basically one well-known public relay operator (Frank Denis on Fastly Compute, default in dnscrypt-proxy). I built a second one and the client to talk to it.

Show HN: Running the second public ODoH relay

Every privacy-focused DNS service requires an account: NextDNS, Cloudflare for Families, Apple's iCloud Private Relay (paid, iOS-only). The protocol that doesn’t require one - ODoH - had basically one well-known public relay operator (Frank Denis on Fastly Compute, default in dnscrypt-proxy). I built a second one and the client to talk to it.

Show HN: I asked AI to write Sci-Fi for eternity

Show HN: Torrix, self hosted, LLM Observability,(no Postgres, no Redis)

I work as a SAP Integration consultant and built this as a side project. Friction point: Most self hosted LLM observability tools require Postgres, Redis and non trivial infrastructure. Teams just want to see what their agents are actually doing in Production, that set up cost discorages adoption. Torrix runs as a single docker contained backed by SQLite. The full install is:<p>curl -o docker-compose.yml <a href="https://raw.githubusercontent.com/torrix-ai/install/main/doc" rel="nofollow">https://raw.githubusercontent.com/torrix-ai/install/main/doc</a>... docker compose up<p>No external dependencies. All data stays in a local SQLite file on your machine.<p>It logs LLM calls through a HTTP proxy or a python/Node SDK : tokens, cost, latency, full prompt and response traces, reasoning token capture. Works with OpenAI, Anthropic, Gemini, Groq, Mistral, Azure Open AI and any Apen AI compatible end point.<p>Things I added as I actually used it on real agent pipelines: cost forecasting and hard budget caps, PII masking, model routing rules, evals with golden runs, AI judge, a prompt library with version history, run tags for filtering by environment, MCP server so AI Assistants can query your own logs and OTLP/HTTP ingestion for apps aöready using OpenTelemetry.<p>Community edition is free for one user with 7-day retention. Pro adds teams, RBAC, 30 day retention, API key management, full text search and audit logs.<p>SQLite doesn't scale to high write throughput. This is aimed at teams logging hundreds to low thousands of LLM calls per day, not millions. Happy to hear what people think and what is missing.<p>GitHub / install: <a href="https://github.com/torrix-ai/install" rel="nofollow">https://github.com/torrix-ai/install</a> Website: <a href="https://www.torrix.ai" rel="nofollow">https://www.torrix.ai</a>

Show HN: Torrix, self hosted, LLM Observability,(no Postgres, no Redis)

I work as a SAP Integration consultant and built this as a side project. Friction point: Most self hosted LLM observability tools require Postgres, Redis and non trivial infrastructure. Teams just want to see what their agents are actually doing in Production, that set up cost discorages adoption. Torrix runs as a single docker contained backed by SQLite. The full install is:<p>curl -o docker-compose.yml <a href="https://raw.githubusercontent.com/torrix-ai/install/main/doc" rel="nofollow">https://raw.githubusercontent.com/torrix-ai/install/main/doc</a>... docker compose up<p>No external dependencies. All data stays in a local SQLite file on your machine.<p>It logs LLM calls through a HTTP proxy or a python/Node SDK : tokens, cost, latency, full prompt and response traces, reasoning token capture. Works with OpenAI, Anthropic, Gemini, Groq, Mistral, Azure Open AI and any Apen AI compatible end point.<p>Things I added as I actually used it on real agent pipelines: cost forecasting and hard budget caps, PII masking, model routing rules, evals with golden runs, AI judge, a prompt library with version history, run tags for filtering by environment, MCP server so AI Assistants can query your own logs and OTLP/HTTP ingestion for apps aöready using OpenTelemetry.<p>Community edition is free for one user with 7-day retention. Pro adds teams, RBAC, 30 day retention, API key management, full text search and audit logs.<p>SQLite doesn't scale to high write throughput. This is aimed at teams logging hundreds to low thousands of LLM calls per day, not millions. Happy to hear what people think and what is missing.<p>GitHub / install: <a href="https://github.com/torrix-ai/install" rel="nofollow">https://github.com/torrix-ai/install</a> Website: <a href="https://www.torrix.ai" rel="nofollow">https://www.torrix.ai</a>

Show HN: E2a – Open-source email gateway for AI agents

We were building an agent system and wanted email as a trigger. We decided to take it out and made it a standalone service.<p>The primary email features we wanted and used for our own agent system:<p>1. Email threading stays consistent with agent conversation threading<p>2. Human in the loop review for outbound emails (especially during testing phase)<p>3. Quick onboarding/offboarding email addresses for agents within minutes<p>4. Websocket for local agents and at-least-once webhook delivery for Cloud agents<p>Not yet: DMARC (only SPF/DKIM today), scoped API keys, HA/multi-region (single VM + single Postgres), app-layer email data encryption, compliance attestations (SOC 2/HIPAA).<p>GitHub: <a href="https://github.com/Mnexa-AI/e2a" rel="nofollow">https://github.com/Mnexa-AI/e2a</a><p>Hosted: <a href="https://e2a.dev/" rel="nofollow">https://e2a.dev/</a><p>Appreciate any feedback / contributions.

Show HN: Gigacatalyst – Extend your SaaS with an embedded AI builder

Hi HN, I’m Namanyay from Gigacatalyst (link: <a href="https://gigacatalyst.com/">https://gigacatalyst.com/</a>). 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.<p>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.<p>But what if <i>everyone</i> 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.<p>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.<p>Here’s what it looks like in action: <a href="https://www.youtube.com/watch?v=_taSpSphH6E" rel="nofollow">https://www.youtube.com/watch?v=_taSpSphH6E</a><p>One of our customers, a Series B company, saw their users (<i>not engineers</i> - managers, ops people, facility directors) build critical workflows like:<p>- Parts stockout prevention: A maintenance manager typed <i>"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."</i> The app tracks consumption velocity, forecasts stockouts, and alerts before it's too late. He says it's prevented ~$500K in emergency downtime.<p>- Invoice OCR from phone photos: Technicians kept losing paper invoices. The prompt: <i>"upload a photo of the invoice, extract vendor name, date, amount, and line items, then match it to the purchase order and flag discrepancies."</i> Now techs snap a photo on-site to automatically add to the system of record.<p>- 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.<p>How Gigacatalyst works under the hood:<p>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.<p>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.<p>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.<p>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.<p>After 2000+ daily users, 900+ apps built, and 70% 30-day retention, today we're opening a public demo.<p>Try it: <a href="https://app.gigacatalyst.com/">https://app.gigacatalyst.com/</a> - enter your SaaS product's API URL (or just the homepage) and start prompting.<p>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 <a href="https://gigacatalyst.com/#contact">https://gigacatalyst.com/#contact</a> and I'll help your team and customers build new functionality on top of your platform.<p>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!

Show HN: Gigacatalyst – Extend your SaaS with an embedded AI builder

Hi HN, I’m Namanyay from Gigacatalyst (link: <a href="https://gigacatalyst.com/">https://gigacatalyst.com/</a>). 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.<p>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.<p>But what if <i>everyone</i> 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.<p>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.<p>Here’s what it looks like in action: <a href="https://www.youtube.com/watch?v=_taSpSphH6E" rel="nofollow">https://www.youtube.com/watch?v=_taSpSphH6E</a><p>One of our customers, a Series B company, saw their users (<i>not engineers</i> - managers, ops people, facility directors) build critical workflows like:<p>- Parts stockout prevention: A maintenance manager typed <i>"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."</i> The app tracks consumption velocity, forecasts stockouts, and alerts before it's too late. He says it's prevented ~$500K in emergency downtime.<p>- Invoice OCR from phone photos: Technicians kept losing paper invoices. The prompt: <i>"upload a photo of the invoice, extract vendor name, date, amount, and line items, then match it to the purchase order and flag discrepancies."</i> Now techs snap a photo on-site to automatically add to the system of record.<p>- 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.<p>How Gigacatalyst works under the hood:<p>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.<p>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.<p>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.<p>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.<p>After 2000+ daily users, 900+ apps built, and 70% 30-day retention, today we're opening a public demo.<p>Try it: <a href="https://app.gigacatalyst.com/">https://app.gigacatalyst.com/</a> - enter your SaaS product's API URL (or just the homepage) and start prompting.<p>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 <a href="https://gigacatalyst.com/#contact">https://gigacatalyst.com/#contact</a> and I'll help your team and customers build new functionality on top of your platform.<p>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!

Show HN: Gigacatalyst – Extend your SaaS with an embedded AI builder

Hi HN, I’m Namanyay from Gigacatalyst (link: <a href="https://gigacatalyst.com/">https://gigacatalyst.com/</a>). 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.<p>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.<p>But what if <i>everyone</i> 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.<p>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.<p>Here’s what it looks like in action: <a href="https://www.youtube.com/watch?v=_taSpSphH6E" rel="nofollow">https://www.youtube.com/watch?v=_taSpSphH6E</a><p>One of our customers, a Series B company, saw their users (<i>not engineers</i> - managers, ops people, facility directors) build critical workflows like:<p>- Parts stockout prevention: A maintenance manager typed <i>"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."</i> The app tracks consumption velocity, forecasts stockouts, and alerts before it's too late. He says it's prevented ~$500K in emergency downtime.<p>- Invoice OCR from phone photos: Technicians kept losing paper invoices. The prompt: <i>"upload a photo of the invoice, extract vendor name, date, amount, and line items, then match it to the purchase order and flag discrepancies."</i> Now techs snap a photo on-site to automatically add to the system of record.<p>- 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.<p>How Gigacatalyst works under the hood:<p>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.<p>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.<p>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.<p>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.<p>After 2000+ daily users, 900+ apps built, and 70% 30-day retention, today we're opening a public demo.<p>Try it: <a href="https://app.gigacatalyst.com/">https://app.gigacatalyst.com/</a> - enter your SaaS product's API URL (or just the homepage) and start prompting.<p>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 <a href="https://gigacatalyst.com/#contact">https://gigacatalyst.com/#contact</a> and I'll help your team and customers build new functionality on top of your platform.<p>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!

Show HN: Gigacatalyst – Extend your SaaS with an embedded AI builder

Hi HN, I’m Namanyay from Gigacatalyst (link: <a href="https://gigacatalyst.com/">https://gigacatalyst.com/</a>). 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.<p>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.<p>But what if <i>everyone</i> 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.<p>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.<p>Here’s what it looks like in action: <a href="https://www.youtube.com/watch?v=_taSpSphH6E" rel="nofollow">https://www.youtube.com/watch?v=_taSpSphH6E</a><p>One of our customers, a Series B company, saw their users (<i>not engineers</i> - managers, ops people, facility directors) build critical workflows like:<p>- Parts stockout prevention: A maintenance manager typed <i>"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."</i> The app tracks consumption velocity, forecasts stockouts, and alerts before it's too late. He says it's prevented ~$500K in emergency downtime.<p>- Invoice OCR from phone photos: Technicians kept losing paper invoices. The prompt: <i>"upload a photo of the invoice, extract vendor name, date, amount, and line items, then match it to the purchase order and flag discrepancies."</i> Now techs snap a photo on-site to automatically add to the system of record.<p>- 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.<p>How Gigacatalyst works under the hood:<p>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.<p>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.<p>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.<p>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.<p>After 2000+ daily users, 900+ apps built, and 70% 30-day retention, today we're opening a public demo.<p>Try it: <a href="https://app.gigacatalyst.com/">https://app.gigacatalyst.com/</a> - enter your SaaS product's API URL (or just the homepage) and start prompting.<p>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 <a href="https://gigacatalyst.com/#contact">https://gigacatalyst.com/#contact</a> and I'll help your team and customers build new functionality on top of your platform.<p>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!

Show HN: Gigacatalyst – Extend your SaaS with an embedded AI builder

Hi HN, I’m Namanyay from Gigacatalyst (link: <a href="https://gigacatalyst.com/">https://gigacatalyst.com/</a>). 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.<p>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.<p>But what if <i>everyone</i> 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.<p>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.<p>Here’s what it looks like in action: <a href="https://www.youtube.com/watch?v=_taSpSphH6E" rel="nofollow">https://www.youtube.com/watch?v=_taSpSphH6E</a><p>One of our customers, a Series B company, saw their users (<i>not engineers</i> - managers, ops people, facility directors) build critical workflows like:<p>- Parts stockout prevention: A maintenance manager typed <i>"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."</i> The app tracks consumption velocity, forecasts stockouts, and alerts before it's too late. He says it's prevented ~$500K in emergency downtime.<p>- Invoice OCR from phone photos: Technicians kept losing paper invoices. The prompt: <i>"upload a photo of the invoice, extract vendor name, date, amount, and line items, then match it to the purchase order and flag discrepancies."</i> Now techs snap a photo on-site to automatically add to the system of record.<p>- 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.<p>How Gigacatalyst works under the hood:<p>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.<p>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.<p>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.<p>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.<p>After 2000+ daily users, 900+ apps built, and 70% 30-day retention, today we're opening a public demo.<p>Try it: <a href="https://app.gigacatalyst.com/">https://app.gigacatalyst.com/</a> - enter your SaaS product's API URL (or just the homepage) and start prompting.<p>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 <a href="https://gigacatalyst.com/#contact">https://gigacatalyst.com/#contact</a> and I'll help your team and customers build new functionality on top of your platform.<p>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!

Show HN: Agentic interface for mainframes and COBOL

Hi HN, we’re Sai and Aayush, and we’re building Hypercubic (<a href="https://www.hypercubic.ai/">https://www.hypercubic.ai/</a>), bringing AI tools to the mainframe and COBOL world. (We did a Launch HN last year: <a href="https://news.ycombinator.com/item?id=45877517">https://news.ycombinator.com/item?id=45877517</a>.) Today we’re launching Hopper, an agentic development environment for mainframes.<p>You can download it here: <a href="https://www.hypercubic.ai/hopper">https://www.hypercubic.ai/hopper</a>, and you can also request access and immediately get a mainframe user account to play with.<p>There's also a video runthrough at <a href="https://www.youtube.com/watch?v=q81L5DcfBvE" rel="nofollow">https://www.youtube.com/watch?v=q81L5DcfBvE</a>.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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.<p>For example, here is a tiny version of the kind of thing Hopper can help debug:<p><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 "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 </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.<p>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.<p>Sensitive operations require approval, and the terminal remains visible at all times.<p>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.<p>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

Hi HN, we’re Sai and Aayush, and we’re building Hypercubic (<a href="https://www.hypercubic.ai/">https://www.hypercubic.ai/</a>), bringing AI tools to the mainframe and COBOL world. (We did a Launch HN last year: <a href="https://news.ycombinator.com/item?id=45877517">https://news.ycombinator.com/item?id=45877517</a>.) Today we’re launching Hopper, an agentic development environment for mainframes.<p>You can download it here: <a href="https://www.hypercubic.ai/hopper">https://www.hypercubic.ai/hopper</a>, and you can also request access and immediately get a mainframe user account to play with.<p>There's also a video runthrough at <a href="https://www.youtube.com/watch?v=q81L5DcfBvE" rel="nofollow">https://www.youtube.com/watch?v=q81L5DcfBvE</a>.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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.<p>For example, here is a tiny version of the kind of thing Hopper can help debug:<p><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 "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 </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.<p>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.<p>Sensitive operations require approval, and the terminal remains visible at all times.<p>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.<p>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

Hi HN, we’re Sai and Aayush, and we’re building Hypercubic (<a href="https://www.hypercubic.ai/">https://www.hypercubic.ai/</a>), bringing AI tools to the mainframe and COBOL world. (We did a Launch HN last year: <a href="https://news.ycombinator.com/item?id=45877517">https://news.ycombinator.com/item?id=45877517</a>.) Today we’re launching Hopper, an agentic development environment for mainframes.<p>You can download it here: <a href="https://www.hypercubic.ai/hopper">https://www.hypercubic.ai/hopper</a>, and you can also request access and immediately get a mainframe user account to play with.<p>There's also a video runthrough at <a href="https://www.youtube.com/watch?v=q81L5DcfBvE" rel="nofollow">https://www.youtube.com/watch?v=q81L5DcfBvE</a>.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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.<p>For example, here is a tiny version of the kind of thing Hopper can help debug:<p><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 "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 </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.<p>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.<p>Sensitive operations require approval, and the terminal remains visible at all times.<p>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.<p>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

Hi HN, we’re Sai and Aayush, and we’re building Hypercubic (<a href="https://www.hypercubic.ai/">https://www.hypercubic.ai/</a>), bringing AI tools to the mainframe and COBOL world. (We did a Launch HN last year: <a href="https://news.ycombinator.com/item?id=45877517">https://news.ycombinator.com/item?id=45877517</a>.) Today we’re launching Hopper, an agentic development environment for mainframes.<p>You can download it here: <a href="https://www.hypercubic.ai/hopper">https://www.hypercubic.ai/hopper</a>, and you can also request access and immediately get a mainframe user account to play with.<p>There's also a video runthrough at <a href="https://www.youtube.com/watch?v=q81L5DcfBvE" rel="nofollow">https://www.youtube.com/watch?v=q81L5DcfBvE</a>.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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.<p>For example, here is a tiny version of the kind of thing Hopper can help debug:<p><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 "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 </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.<p>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.<p>Sensitive operations require approval, and the terminal remains visible at all times.<p>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.<p>We’re curious to hear your thoughts! especially from anyone who has worked with mainframes, COBOL or has done legacy enterprise modernization.