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    Together AI reportedly raises $800 million at an $8.3 billion valuation

    A post also describes a walkthrough of the product team’s shared repository, with sections on context ownership, live feature research and product requirements documents.

    Aakash GuptaAG
    1 Source, 23d ago, first seen 23d ago

    TLDR

    A post says Together AI raised $800 million at an $8.3 billion valuation and describes a walkthrough of the repository its product team uses. The listed topics include who owns context files, team versus personal skills, human oversight, product requirements documents, agent evaluations on shipped features and workflow costs.

    Combined views

    24K

    1 Source, first seen 23d ago

    Combined views

    24K

    1 Source, first seen 23d ago

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    5 comments
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    1 Source

    Aakash Gupta@aakashguptaTogether AI just raised $800M at an $8.3B valuation. I got their product team to open up the actual repo they run on: 0:00 - Slop is the new party foul 1:44 - Why individual output backfired 3:26 - Inside the shared product repo 8:57 - Who owns the context files 10:41 - Team skills vs personal skills 11:57 - Picking a harness and a model 15:17 - Feature research running live 16:57 - What stays human in the loop 19:56 - The PRD skill that interviews you 23:09 - Half a day of research in 5 min 24:25 - Their customer insights MCP 27:31 - What a good PRD looks like now 32:02 - Reading the finished PRD 35:07 - Every repo in one place 40:24 - Context is a hierarchy, not a pool 41:20 - Build your own orchestrator 44:19 - Agent evals on shipped features 49:41 - Design validated every few hours 52:26 - Where PM ends, engineer begins 55:10 - What it actually cost them23d

    1 Source

    Aakash Gupta@aakashguptaTogether AI just raised $800M at an $8.3B valuation. I got their product team to open up the actual repo they run on: 0:00 - Slop is the new party foul 1:44 - Why individual output backfired 3:26 - Inside the shared product repo 8:57 - Who owns the context files 10:41 - Team skills vs personal skills 11:57 - Picking a harness and a model 15:17 - Feature research running live 16:57 - What stays human in the loop 19:56 - The PRD skill that interviews you 23:09 - Half a day of research in 5 min 24:25 - Their customer insights MCP 27:31 - What a good PRD looks like now 32:02 - Reading the finished PRD 35:07 - Every repo in one place 40:24 - Context is a hierarchy, not a pool 41:20 - Build your own orchestrator 44:19 - Agent evals on shipped features 49:41 - Design validated every few hours 52:26 - Where PM ends, engineer begins 55:10 - What it actually cost them23d