Tryardent
Hey HN! We’re Vikram and Evan from Ardent (https://tryardent.com). We're building database sandboxes for you and your coding agents. In the last two years coding agents have gotten dramatically more capable at handling complex engineering tasks. But without access to a realistic sandbox at the DB layer for testing, they ship garbage that can take down production databases. I spent over a year building an AI Data Engineer that failed for this exact reason. Evan spent the last 12 years in data engineering and hit this wall building agents at his last company. Ardent was built to make it possible for coding agents to get near instant access to production-like sandboxes so they can test their work. To do this we write a replication stream out of the target DB, scaling with kafka onto a read replica with copy on write enabled and autoscaling compute (we currently prefer neon as a primary branching engine due to their implementation of these properties). Our replication stream uses logical replication + ddl triggers to enable usage on any hosted postgres DB since most platforms do not allow physical replication which is traditionally used for creating replicas. This provides a few primary benefits: 1. Does not require a platform migration to a DB provider like neon, allowing strong separation of production and development concerns. 2. Minimal impact on the production database while allowing clones to spin up in <6s, even at TB scale with copy-on-write Security matters a lot with cloning production so we run a proxy layer to generate custom postgres URLs and route all connections to allow more granular access control to clones, prevent credential leak, and follow a split plane architecture to allow full data residency on your cloud through BYOC. We also support anonymization through the ability to register SQL that runs on branches before they are returned. This has been used for PII redaction and branch modification. Our goal is to make every data infrastructure platform “cloneable” in one place so agents can fully test the impact of their changes on production like data environments without risk. Here's a demo of it: https://youtu.be/5S1kwPtiRU0 We’d love to understand how you work with coding agents on the DB and if you try Ardent (it's free to get started) what worked, what broke and what’s missing.
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Headless Cloud Security
The cloud security company I work for, Sysdig, launched “Headless Cloud Security” last week. The short version: as attacks get faster and more automated, security tooling is going to need to evolve beyond dashboards and humans clicking through workflows all day. We’ve already seen “headless” models emerge in other categories, and engineering teams are rapidly adopting agentic and CLI-first workflows with tools like Claude Code, Cursor, and MCP servers. Security teams, historically, tend to lag engineering adoption curves by 6–18 months, but I don’t think that gap will hold much longer. The idea behind headless security is that security capabilities should be consumable programmatically — through APIs, AI agents, IDEs, CI/CD pipelines, and automated workflows — not just through a UI. This post covers it in more detail: https://www.sysdig.com/learn-cloud-native/what-is-headless-c... Curious whether others here are seeing similar shifts inside their orgs, especially around AI-assisted development and security operations.
Agentic interface for mainframes and COBOL
Hi HN, we’re Sai and Aayush, and we’re 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’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.
Open-source 2D IDE for managing agent CLIs
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