outcomeops

Stop babysitting AI output

AI Engineers
Who Ship
Autonomous agents that clear your backlog—following YOUR standards, not generic patterns.

Zero IP exfiltratio

[outcomeops @outcomeops]

I spent 14 years building internal developer platforms.

Then AI came… and we’re repeating the exact same mistakes.

Prompts instead of tickets.
Hallucinations instead of standards.
Vibe coding instead of outcomes.

This is the manifesto for what comes next.

“DevOps is Dead” — the full origin story.

Visit us at https://t.co/Si5jrQ8huJ

#AI #PlatformEngineering #OutcomeOps

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@Ryan_Ravenson @cityuponahell @claudeai Ryan, I'm not sure what you think you are proving? Science allows itself to LEARN new things based on research. The bible hasn't changed because it's fake. Even the Constitution has changed since it's inception. I trust the thing that 'learns' vs the thing that doesn't.

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@ku_ds17868 https://t.co/8P5Dv8Rn1M Max x20

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@Ryan_Ravenson @cityuponahell @claudeai You mean it uses science, math and peer verified research and not "what the Bible says" then yep

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@500yoChristian @claudeai You mean based on science, math, and per verified research and not "what the Bible says" then yes

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@DanielSmidstrup Squats.

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@ClaudeDevs I was about to hit my weekly limit I almost had to stop working for the day to go watch Netflix lol.

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@MrBeast 999,999

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This is why OutcomeOps + OpenAI on Bedrock are genuinely better together.
Not because we picked a winner. Because we made model choice reversible, auditable, and low-friction — even when procurement or compliance requirements shift.

Full post with the production war stories and more examples:
https://t.co/ueuMRvVqQD

Which model are you actually standardizing on right now — and how painful would it be to swap it?

[outcomeops @outcomeops]

OpenAI’s GPT-5.5 is now generally available on Amazon Bedrock.

For most teams this is “cool, another model.” For anyone operating in regulated environments, it just removed a major procurement blocker.

But the real story isn’t the model. It’s what happens when you have the right layer on top of it.

[outcomeops @outcomeops]

When the model changes, what actually moves?
Nothing that matters:

* Your corpus and retrieval pipeline
* Role-scoped workspaces
* Full audit trail (model used, prompt, response — OCSF exportable)
* Compliance posture and guardrails

The model is the commodity. The dispatch layer is plumbing. The context is the moat.

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@AnthropicAI Excited!!

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I don't run a laptop I run a GPU server with 20 tmux sessions running claude code . It's a bit of a legacy box but it works.

CPU
- 2× Intel Xeon Platinum 8168 @ 2.70 GHz (3.7 GHz turbo)
- 24 cores/socket × 2 sockets × 2 threads = 96 logical CPUs (48 physical cores)

Memory
- 123 GiB RAM (32 GiB used, 91 GiB available, 78 GiB in buff/cache)
- 8 GiB swap (2.4 GiB used)

GPUs
- NVIDIA RTX 5090 (GPU 0, AD102-class)
- NVIDIA RTX 4090 (GPU 1, AD102)
- Plus an onboard ASPEED BMC display adapter

Storage (~2 TB total across two md RAID arrays)

[outcomeops @outcomeops]

The pipeline didn’t fail. The wiki did.

When an engineer follows inaccurate advice from an AI that was reading an outdated internal wiki, the root cause isn’t the model. It’s the missing organizational intelligence layer.

New post on what that layer actually looks like:
https://t.co/kJaAnCB0nr

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@unimaginative AI Enterprise Search Across All Your Platforms

https://t.co/PTmm7y1dGa

[outcomeops @outcomeops]

@mjbuildr Add me! Follow me! I'm building an Enterprise Powered AI Search platform. It enabled search against Github, Confluence, Jira, Outlook, MS teams, etc..

https://t.co/PTmm7y1dGa

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@DanielSmidstrup Put the dishes in the dishwasher.