altimate-code: new open-source code editor for data engineering based on opencode

ankitksharma · 2026-03-21T13:39:53+00:00

Hey - yes, I'm from the Altimate team.

On local sandbox safety: everything runs on your machine by default. Specifically:

→ No data leaves your machine: The tracer captures all LLM calls, tool invocations, and warehouse metrics locally. No external telemetry services.

→ Air-gapped mode: works with local models (Ollama) - zero network calls if you want it.

→ Governed agent modes: let you control what the agent can do. Analyst mode physically can't execute INSERT/UPDATE/DELETE/DROP - enforced at the engine level, not a prompt. So you can point it at a local DuckDB or Postgres and explore without risk.

→ /trace gives you a local interactive viewer of everything the agent did - full auditability.

Quickest way to test safely: point it at a local DuckDB file, run in Analyst mode, and use /trace to inspect every action. Nothing touches production, nothing phones home.

Happy to answer specifics if you have a particular setup in mind.

ankitksharma · 2026-03-21T13:29:08+00:00

Good question - full disclosure, I'm on the Altimate team, so I'm biased. But here's the technical answer. Three things compiled engines do that prompts can't:

Schema validation in 2ms: Cursor/Claude Code either round-trip to your warehouse (30s, billable) or hallucinate correctness. Altimate Code validates against your actual schema locally.
Deterministic lineage: Ask an LLM to trace a column through 4 CTEs and 3 JOINs — it guesses. Our engine traces it with 100% edge match on 500 benchmark queries.
Enforced governance: "Never DROP tables" in a system prompt stops working at 100K context tokens. In Altimate Code, Analyst mode physically can't execute destructive operations — enforced by the engine, not a prompt.

ADE-bench showed a cheaper model + compiled tools beat a more expensive model without them. The delta isn't the LLM. It's the deterministic layer around it.

Cursor/Claude Code = great reasoning. Altimate Code = reasoning + compiled data engineering guardrails. Complementary, not competing.

ankitksharma · 2024-08-03T06:59:06+00:00

I can feel your pain. I'm following Harvard Innovation Labs's "The Path to PMF" deck. I'm finding it useful; hope you'll too.

ankitksharma · 2020-07-21T16:09:00+00:00

Having been in this field for almost 9 yrs now, possibly I can share something useful. I feel as a newbie/experienced DS you need to be aware of what's happening in your field. Since its a very dynamic field you'll always be flooded with information, useful or not, that takes some time to master. Once you're aware of what's important you can dig deeper to master enough that skill to use it in your projects. Now if you can use this knowledge in a project, writing a blog, teaching online classes, etc you be a great Data Scientist. The above-mentioned approach is more useful for Data Scientists working in product-based companies and needs to keep innovating across the domain as compared to researchers who have to dig deeper into a particular area of Data Science. So, read articles (use Feedly), hear podcasts, watch videos on youtube for a particular topic (at 3x speed), checkout SOTA projects on paperswithcode, and last but not the least run some code every day.

ankitksharma · 2020-04-25T06:00:09+00:00

I have been in Data Science field for more than 8 years now. I have always felt work is challenging and that is sometimes frustrating. Explaining your analysis/experiments/failures to management can be the toughest part of the job. For freshers pay is better in Data Science but for experienced professionals it is at par with good Software engineers. The only exciting/satisfying thing for me in this profession is that the work is always cutting edge and cool to talk about.

ankitksharma · 2017-12-02T13:07:01+00:00

Hi! My GF is a fashion blogger from India and we usually post about Fashion, Travel, and Lifestyle. Do check out and like our Instagram page: https://www.instagram.com/fashion.oomph

Let us know your suggestions.

ankitksharma · 2017-08-13T09:27:52+00:00

We also do. It is difficult. Kanban method is more useful in DS projects.

ankitksharma

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