Hypercubic is an AI platform that helps Fortune 500 companies understand, preserve, and modernize their mainframe systems. These are the systems that run COBOL from the 1960s that still quietly power banking, insurance, airlines, and governments today.
70% of the Fortune 500 still run on mainframes, but the engineers who built and maintained them are retiring. Today, the average age of a COBOL/mainframe engineer is about 55 and rapidly increasing. What\u2019s left behind are opaque, black box systems with almost no one who understands how they work. Modernization projects often fail, documentation is decades out of date, and critical institutional knowledge lives only in the minds of a few senior subject matter experts who are now leaving the workforce.
Current \u201cAI for code\u201d tools focus on codebases and repositories, so they miss the unwritten rules, historical context, and architectural reasoning that live in human minds. In the COBOL/mainframe world, that institutional knowledge is the key missing piece.
What we heard from modernization leaders is that the hard part is not the code analysis. The challenge is the institutional knowledge that never made it into code or documentation and has walked out the door. Modernization projects fail not because no one can parse COBOL, but because no one can answer \u201cwhy was this billing edge case added in 1995 and what breaks if we remove it.\u201d
Hypercubic is building an AI-native maintenance and modernization platform that learns how legacy mainframe systems actually work and captures the human reasoning behind operating them. We\u2019re doing this with two initial tools, HyperDocs and HyperTwin.
HyperDocs ingests COBOL, JCL, and PL/I codebases to generate documentation, architecture diagrams, and dependency graphs. Enterprises currently spend months or years hiring contractors to reverse-engineer these systems; HyperDocs compresses that work to take much less time.
COBOL was designed to resemble English and business prose, making it a good fit for LLMs today. Mainframes have decades of consistent patterns (COBOL, JCL, CICS, batch jobs) and a finite set of recurring tasks (such as payroll, transaction processing, billing).
For example, here\u2019s a billing fragment that would be run nightly in production at large insurance companies for moving money, closing accounts, and triggering downstream reports:
EVALUATE TRUE\n WHEN PAYMENT-DUE AND NOT PAID\n PERFORM CALCULATE-LATE-FEE\n PERFORM GENERATE-NOTICE\n WHEN PAYMENT-RECEIVED AND BALANCE-DUE = 0\n MOVE "ACCOUNT CLEAR" TO STATUS\n PERFORM ARCHIVE-STATEMENT\n WHEN OTHER\n PERFORM LOG-ANOMALY\n END-EVALUATE.\nOur other tool, HyperTwin, tackles the \u201ctribal knowledge\u201d problem. It learns directly from subject-matter experts, observing workflows, analyzing screen interactions, and conducting AI-driven interviews to capture how they debug and reason about their systems. The goal is to build digital \u201ctwins\u201d of the experts on how they debug, architect, and maintain these systems in practice.
Together, HyperDocs and HyperTwin form a knowledge graph of legacy systems linking code, systems, and human reasoning.
Here\u2019s a demo video of our HyperTwin product: https://www.youtube.com/watch?v=C-tNtl9Z_jY
You can explore our documentation platform, including examples from the AWS Card Demo (a widely used COBOL codebase example) and a dummy insurance project here:\nhttps://hyperdocs-public.onrender.com/.
e.g. Developer perspective docs - High level system architecture of credit card management: \nhttps://hyperdocs-public.onrender.com/docs/aws-carddemo-with...
We\u2019re curious to hear your thoughts and feedback, especially from anyone who\u2019s worked with mainframes or tried to modernize legacy systems."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["hypercubic"],"value":"Launch HN: Hypercubic (YC F25) \u2013 AI for COBOL and Mainframes"}},"_tags":["story","author_sai18","story_45877517","launch_hn"],"author":"sai18","children":[45878105,45878211,45878280,45878485,45878736,45878988,45878999,45879239,45879454,45879545,45879611,45879647,45879692,45879750,45880593,45881146,45881407,45882020,45884132,45886103,45891300,45902423],"created_at":"2025-11-10T16:23:24Z","created_at_i":1762791804,"num_comments":58,"objectID":"45877517","points":91,"story_id":45877517,"story_text":"Hi HN, we\u2019re Sai and Aayush and we\u2019re building Hypercubic (https://www.hypercubic.ai/)!
Hypercubic is an AI platform that helps Fortune 500 companies understand, preserve, and modernize their mainframe systems. These are the systems that run COBOL from the 1960s that still quietly power banking, insurance, airlines, and governments today.
70% of the Fortune 500 still run on mainframes, but the engineers who built and maintained them are retiring. Today, the average age of a COBOL/mainframe engineer is about 55 and rapidly increasing. What\u2019s left behind are opaque, black box systems with almost no one who understands how they work. Modernization projects often fail, documentation is decades out of date, and critical institutional knowledge lives only in the minds of a few senior subject matter experts who are now leaving the workforce.
Current \u201cAI for code\u201d tools focus on codebases and repositories, so they miss the unwritten rules, historical context, and architectural reasoning that live in human minds. In the COBOL/mainframe world, that institutional knowledge is the key missing piece.
What we heard from modernization leaders is that the hard part is not the code analysis. The challenge is the institutional knowledge that never made it into code or documentation and has walked out the door. Modernization projects fail not because no one can parse COBOL, but because no one can answer \u201cwhy was this billing edge case added in 1995 and what breaks if we remove it.\u201d
Hypercubic is building an AI-native maintenance and modernization platform that learns how legacy mainframe systems actually work and captures the human reasoning behind operating them. We\u2019re doing this with two initial tools, HyperDocs and HyperTwin.
HyperDocs ingests COBOL, JCL, and PL/I codebases to generate documentation, architecture diagrams, and dependency graphs. Enterprises currently spend months or years hiring contractors to reverse-engineer these systems; HyperDocs compresses that work to take much less time.
COBOL was designed to resemble English and business prose, making it a good fit for LLMs today. Mainframes have decades of consistent patterns (COBOL, JCL, CICS, batch jobs) and a finite set of recurring tasks (such as payroll, transaction processing, billing).
For example, here\u2019s a billing fragment that would be run nightly in production at large insurance companies for moving money, closing accounts, and triggering downstream reports:
EVALUATE TRUE\n WHEN PAYMENT-DUE AND NOT PAID\n PERFORM CALCULATE-LATE-FEE\n PERFORM GENERATE-NOTICE\n WHEN PAYMENT-RECEIVED AND BALANCE-DUE = 0\n MOVE "ACCOUNT CLEAR" TO STATUS\n PERFORM ARCHIVE-STATEMENT\n WHEN OTHER\n PERFORM LOG-ANOMALY\n END-EVALUATE.\nOur other tool, HyperTwin, tackles the \u201ctribal knowledge\u201d problem. It learns directly from subject-matter experts, observing workflows, analyzing screen interactions, and conducting AI-driven interviews to capture how they debug and reason about their systems. The goal is to build digital \u201ctwins\u201d of the experts on how they debug, architect, and maintain these systems in practice.
Together, HyperDocs and HyperTwin form a knowledge graph of legacy systems linking code, systems, and human reasoning.
Here\u2019s a demo video of our HyperTwin product: https://www.youtube.com/watch?v=C-tNtl9Z_jY
You can explore our documentation platform, including examples from the AWS Card Demo (a widely used COBOL codebase example) and a dummy insurance project here:\nhttps://hyperdocs-public.onrender.com/.
e.g. Developer perspective docs - High level system architecture of credit card management: \nhttps://hyperdocs-public.onrender.com/docs/aws-carddemo-with...
We\u2019re curious to hear your thoughts and feedback, especially from anyone who\u2019s worked with mainframes or tried to modernize legacy systems.","title":"Launch HN: Hypercubic (YC F25) \u2013 AI for COBOL and Mainframes","updated_at":"2026-03-05T23:01:04Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"sai18"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["hypercubic"],"value":"Hi HN, we\u2019re Sai and Aayush, and we\u2019re building Hypercubic (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\u2019re 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:\n\n IDENTIFICATION DIVISION.\n PROGRAM-ID. PAYCALC.\n\n DATA DIVISION.\n WORKING-STORAGE SECTION.\n 01 CUSTOMER-BALANCE PIC 9(7)V99.\n\n PROCEDURE DIVISION.\n ADD 100.00 TO CUSTOMER-BALNCE\n DISPLAY "UPDATED BALANCE: " CUSTOMER-BALANCE\n STOP RUN.\n\n\n JCL:\n\n //PAYCOMP JOB (ACCT),'COMPILE',CLASS=A,MSGCLASS=X\n \n //COBOL EXEC IGYWCL\n \n [//COBOL.SYSIN](https://cobol.sysin/) DD DSN=USER1.APP.COBOL(PAYCALC),DISP=SHR\n \n [//LKED.SYSLMOD](https://lked.syslmod/) DD DSN=USER1.APP.LOAD(PAYCALC),DISP=SHR\n\nHopper 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\u2019re curious to hear your thoughts! especially from anyone who has worked with mainframes, COBOL or has done legacy enterprise modernization."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Agentic interface for mainframes and COBOL"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["hypercubic"],"value":"https://www.hypercubic.ai/hopper"}},"_tags":["story","author_sai18","story_48111143","show_hn"],"author":"sai18","children":[48111453,48112072,48112318,48112638,48112797,48113106,48113528,48114312,48114316,48114863,48115445,48116044,48119960,48121909,48127147,48130974,48136296],"created_at":"2026-05-12T17:10:22Z","created_at_i":1778605822,"num_comments":51,"objectID":"48111143","points":97,"story_id":48111143,"story_text":"Hi HN, we\u2019re Sai and Aayush, and we\u2019re building Hypercubic (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\u2019re 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:\n\n IDENTIFICATION DIVISION.\n PROGRAM-ID. PAYCALC.\n\n DATA DIVISION.\n WORKING-STORAGE SECTION.\n 01 CUSTOMER-BALANCE PIC 9(7)V99.\n\n PROCEDURE DIVISION.\n ADD 100.00 TO CUSTOMER-BALNCE\n DISPLAY "UPDATED BALANCE: " CUSTOMER-BALANCE\n STOP RUN.\n\n\n JCL:\n\n //PAYCOMP JOB (ACCT),'COMPILE',CLASS=A,MSGCLASS=X\n \n //COBOL EXEC IGYWCL\n \n [//COBOL.SYSIN](https://cobol.sysin/) DD DSN=USER1.APP.COBOL(PAYCALC),DISP=SHR\n \n [//LKED.SYSLMOD](https://lked.syslmod/) DD DSN=USER1.APP.LOAD(PAYCALC),DISP=SHR\n\nHopper 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\u2019re curious to hear your thoughts! especially from anyone who has worked with mainframes, COBOL or has done legacy enterprise modernization.","title":"Show HN: Agentic interface for mainframes and COBOL","updated_at":"2026-08-25T05:02:19Z","url":"https://www.hypercubic.ai/hopper"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"aequatio"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["hypercubic"],"value":"A... n fold orthographic quasicrystal-structured neural network scan behaviour pattern routine that manifests as a new type of tri dimensional artificial intelligence scan behaviour path pattern algorithm.
WHAT IS MSQNN?\nWhat is Morphing Somatic Quasicrystal Neural Network (M.S.Q N.N)? Quintessentially, quasicrystals occur midst nature as multi-dimensional, semiregular uniform n-polytope hypercubic voluminous structures. The core algorithm is dynamic, as such is able to produce n-sized polygons stemming from tesseract to octeract petrie polygon projection, so as to gracefully collapse n dimensional existences into ones of orthographic nature. Different n-sized polygon projections yield multifarious AI path grid pattern configurations. This orthographic resulting manifest's as M.S.Q N.N's base blueprint AI path network\\electron diffraction pattern. It is this condensed electron diffraction pattern nodal collection that is compacted to compose projected path sequences. M.S.Q N.N - Morphing (expansive/contractive), non-abstract, non-hierarchical (somatic) n fold orthographic quasicrystal-structured neural network scan behaviour pattern algorithm manifests as a new type of tri dimensional artificial intelligence scan behaviour path pattern algorithm.
https://github.com/JordanMicahBennett/MORPHING-SOMATIC-QUASICRYSTAL-NEURAL-NETWORK/"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Morphing Somatic Quasicrystal Neural Network"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_aequatio","story_9323117","show_hn"],"author":"aequatio","created_at":"2015-04-05T04:30:09Z","created_at_i":1428208209,"num_comments":0,"objectID":"9323117","points":3,"story_id":9323117,"story_text":"A... n fold orthographic quasicrystal-structured neural network scan behaviour pattern routine that manifests as a new type of tri dimensional artificial intelligence scan behaviour path pattern algorithm.
WHAT IS MSQNN?\nWhat is Morphing Somatic Quasicrystal Neural Network (M.S.Q N.N)? Quintessentially, quasicrystals occur midst nature as multi-dimensional, semiregular uniform n-polytope hypercubic voluminous structures. The core algorithm is dynamic, as such is able to produce n-sized polygons stemming from tesseract to octeract petrie polygon projection, so as to gracefully collapse n dimensional existences into ones of orthographic nature. Different n-sized polygon projections yield multifarious AI path grid pattern configurations. This orthographic resulting manifest's as M.S.Q N.N's base blueprint AI path network\\electron diffraction pattern. It is this condensed electron diffraction pattern nodal collection that is compacted to compose projected path sequences. M.S.Q N.N - Morphing (expansive/contractive), non-abstract, non-hierarchical (somatic) n fold orthographic quasicrystal-structured neural network scan behaviour pattern algorithm manifests as a new type of tri dimensional artificial intelligence scan behaviour path pattern algorithm.
https://github.com/JordanMicahBennett/MORPHING-SOMATIC-QUASICRYSTAL-NEURAL-NETWORK/","title":"Show HN: Morphing Somatic Quasicrystal Neural Network","updated_at":"2023-09-07T02:27:25Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"mycroftiv"},"title":{"matchLevel":"none","matchedWords":[],"value":"Plan 9 Advanced Namespace Tools 5.64 Released"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["hypercubic"],"value":"https://doc.9gridchan.org/blog/191221.hypercubic.release"}},"_tags":["story","author_mycroftiv","story_21913045"],"author":"mycroftiv","created_at":"2019-12-30T12:59:53Z","created_at_i":1577710793,"num_comments":0,"objectID":"21913045","points":5,"story_id":21913045,"title":"Plan 9 Advanced Namespace Tools 5.64 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If you like the project I'd appreciate if you could give it a star on GitHub \u2665 If you have any issue or feature request please submit at https://github.com/ndavd/ncube/issues
-- EDIT DEC13 2023: IMPORTANT NOTICE --
https://ncube.ndavd.com MIGHT BE DOWN FOR YOU.
I deeply appreciate all the attention and feedback that ncube has been having this last day.
Alas it has become too big for the free hosting that I'm currently using and has exceeded my available monthly bandwidth.
I will be fixing it soon. In the meantime I recommend using the native binaries from https://github.com/ndavd/ncube
UPDATE: https://ncube.ndavd.com IS BACK ONLINE and with a performance boost. Should load faster now.
-- EDIT DEC17 2023 --
If ncube is leaving your system unresponsive it should be due to a recent Chromium issue with hardware acceleration affecting at least all Chromium-based web browsers on Linux. In case you're on Brave downgrading to 1.60 fixes it.
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If you like the project I'd appreciate if you could give it a star on GitHub \u2665 If you have any issue or feature request please submit at https://github.com/ndavd/ncube/issues
-- EDIT DEC13 2023: IMPORTANT NOTICE --
https://ncube.ndavd.com MIGHT BE DOWN FOR YOU.
I deeply appreciate all the attention and feedback that ncube has been having this last day.
Alas it has become too big for the free hosting that I'm currently using and has exceeded my available monthly bandwidth.
I will be fixing it soon. In the meantime I recommend using the native binaries from https://github.com/ndavd/ncube
UPDATE: https://ncube.ndavd.com IS BACK ONLINE and with a performance boost. Should load faster now.
-- EDIT DEC17 2023 --
If ncube is leaving your system unresponsive it should be due to a recent Chromium issue with hardware acceleration affecting at least all Chromium-based web browsers on Linux. In case you're on Brave downgrading to 1.60 fixes it.
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