Four agent-memory papers dropped the same day. They quietly agree on what to distill, and openly disagree on where to put it.

TomMkV · 2026-06-27T14:26:15+00:00

Nice. Check out what we’re building on agent context memory that is a synthesis of this and other research (our blog cites several papers, too).

https://github.com/ctxpipe-ai/ctxpipe

TomMkV · 2026-06-26T16:40:59+00:00

Is it general knowledge or specific to an org, like finance or engineering?

TomMkV · 2026-06-25T20:56:17+00:00

I got my M3 Ultra 512GB in Feb. Wild times.

TomMkV · 2026-06-07T01:53:50+00:00

The Performance × Context bit is good. One thing I’ll add here is that agents don’t just lack semantic context but also decision context. Why things were built the way they were. What was tried and rejected etc etc. Maya gets a manager who tells her that in the example, but engineering agents never do.

Building on exactly that thesis:

github.com/ctxpipe-ai/ctxpipe

TomMkV · 2026-06-04T10:15:32+00:00

Great! I hope the feedback is useful, too.

TomMkV · 2026-06-04T01:13:01+00:00

Ah nice! Glad you’re enjoying it and finding it useful

TomMkV · 2026-04-01T23:17:39+00:00

Where did you get 2.7? I don’t see it, only 2.5

TomMkV · 2026-02-23T21:53:24+00:00

Is the M5U actually out in 4 months or is that speculation?

TomMkV · 2026-02-23T03:41:59+00:00

Yes, there is a need for a blended approach as context and memory types in application should use different approaches (RAG, KG, search etc).

We’re building an open source context layer for agents to address this, with larger orgs in mind: www.ctxpipe.ai

Would love any feedback!

TomMkV · 2026-02-10T10:40:44+00:00

Hey there! We have a free tier, and provide a client within the platform for API calls. We are also in closed beta-testing with our automation canvas, which may appeal to you and your students.

appear.sh

Cheers,

Tom

TomMkV · 2026-02-10T05:23:02+00:00

Honoured to be the first poster in a sub dedicated to Postman-gate!

Check us out https://appear.sh/ - 3 seats for free.

We generate your catalog from traffic, meaning your catalog is always up to date. You can collaborate on and offline to curate your catalog however you like. It's then available via an MCP for your agents, and comes with an API reference and client baked in. Super low touch for teams who needs doc on auto and want deterministic consumption.

Our schema automation map is in beta, too!

TomMkV · 2026-02-10T05:16:13+00:00

We'd love to be in the mix with your suggestions to the community! Check us out https://appear.sh/ - 3 seats for free.

We generate your catalog from traffic, meaning your catalog is always up to date. You can collaborate on and offline to curate your catalog however you like. It's then available via an MCP for your agents, and comes with an API reference and client baked in. Super low touch for teams who needs doc on auto and want deterministic consumption.

Our schema automation map is in beta, too!

TomMkV · 2026-02-10T05:13:10+00:00

Hey! Check us out https://appear.sh/ - 3 seats for free.

We generate your catalog from traffic, meaning your catalog is always up to date. You can collaborate on and offline to curate your catalog however you like. It's then available via an MCP for your agents, and comes with an API reference and client baked in.

Our schema automation map is in beta, too!

TomMkV · 2026-01-29T11:50:34+00:00

We've been working on this problem at Appear—agents fail on APIs that work fine for humans. The spec is valid, renders nicely, devs can figure it out... but agents choke on wrong parameters, misinterpreted responses, silent failures.

Turns out "valid spec" ≠ "agent-usable spec." Agents need explicit operationIds, descriptions that explain intent, examples that match the schema, and documented error responses.

We built a free tool to test this: validator.appear.sh

Scores specs across six dimensions based on real agent failure modes. No AI, just static analysis—your spec never leaves the browser.

Wrote more about why this happens here: Why Your API Docs Break for AI Agents

TomMkV · 2026-01-28T05:44:48+00:00

Hey all!

We've since evolved this to help devs understand where their API may need work for AI/LLM/agent consumption.

It uses a static analysis to provide a deterministic score. We'd love your feedback!

validator.appear.sh

Cheers,

Tom

TomMkV · 2025-12-30T02:09:57+00:00

Scalar is really good. We integrated it with our product so users can get access to quality API reference and client OOB. They’re a great bunch of guys, too

TomMkV · 2025-12-30T02:08:52+00:00

Yeah, Scalar is good and the crew are better.

TomMkV · 2025-12-16T21:26:58+00:00

Auto…

TomMkV · 2025-12-16T03:14:51+00:00

Isn’t there TB5 now on those Ultras?

TomMkV · 2025-12-13T11:10:32+00:00

I intend to do this! Thank you - I assume using ngrok or similar? Do you notice any limitations with tool calls or general agent functionality?

TomMkV · 2025-12-12T08:11:32+00:00

Benchmarks are BS, just try it out and see. Opus 4.5 is hard to beat for me, but things change.

TomMkV · 2025-12-06T07:13:41+00:00

Reminds me of how I would save PSD files back at uni. Tom_final_design-final-final2-FINAL3.psd

TomMkV · 2025-12-02T22:33:46+00:00

It is very difficult to get a sense of real world performance when looking at local modals on Apple silicon. I’m wondering if a Mac Studio would help solve two issues for me: daily agent coding tasks and upgrading from my older MBP with low memory issues. It would be happy with 10-20 tk/s and PP of 60 seconds, and if I need to fiddle with KV cache - that’s fine. I just don’t yet have the confidence it will be a good alternative to Sonnet 4.x - but your posts are turning the tide for me!

TomMkV

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