building agents at https://xitter.cf/t.co/zUGUNFBwYS, prev @stripe @mit @medialab

sf via montana
Joined March 2013
Communication is painful for every organization, and there's even a numerical threshold where it immediately starts to break down. Whether you call it a knowledge base, a company brain, or a wiki, you should build a knowledge loop to kill the pain.
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Your agents are only as reliable as the context you provide. But no one knows how accurate their context is. Falconer Knowledge Health solves this. Accurate context means greater speed, less tokens, shorter prompts, and high-quality output from your agents.
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opus as sharp as always, with the "you already overthink this stuff" callout
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how is there still not a good way to have an LLM find me a flight
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on the bus in SF. only 10-20% of people bother to pay at all. regardless of socioeconomic status! guy working on a macbook pro in the back? didn't tap. woman in an arcteryx jacket? didn't tap. dude wearing a tech co sweatshirt and different tech co backpack? didn't tap.
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it took me a second or two to catch the issues, but there are at least 3 blatant ones
OpenAI Image V2 - Maskingtape Alpha failed the test as usual and current all gen also failed it , i hope one day we will get this passed Prompt : A validly scrambled Rubik's cube placed by a mirror, clearly showing its mirror reflection. No harsh light reflections.
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/btw in claude code is incredibly helpful when working on unfamiliar domains. i can let claude run wild and simultaneously ask really in-depth questions with full context. fantastic idea, i hope it makes it to all the harnesses soon.
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We just shipped Tables v2. Here's what changed: - Drag and drop rows and columns - Better keyboard shortcuts - Polished cell interactions - Cell colors
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turns out you don't even need to work at an AI lab to get these unhinged emails "Formal proof: P vs NP, Navier-Stokes and Riemann Hypothesis"
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my favorite part: "at first I didnt knew that these were millionum problems funny, just wanted a challenge"
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Full self-driving mode is finally here 🏎 🦅 Introducing Falconer Update: automatic updates for your docs. Toggle it on to auto-update. When a PR merges, Falconer reads the diff, keeps the docs in sync, and pings you in Slack. Just keep shipping. The docs will keep up.
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software engineers have a lot more free time, now that agents can write the code. but what will they do with all that time?
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so far, it's been pretty tame. build more, build faster, scroll twitter while the agent works but for some, I think this will be hugely freeing. and I'm excited to see what that brings us i wrote a small post about this: kyeb.com/posts/a-mass-decath…
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i've just been calling this the hedonic treadmill. anyone have a better term yet?
anyone who regularly uses the models and has even a remotely vague sense of how programming works knows that models degrade. the more interesting story here is that everyone trusts that the models perform at their prescribed benchmarks until the next one.
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it only cost ~$200 in Opus tokens to migrate Falconer's entire infrastructure from Terraform to Pulumi today
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granted, we're a 1-year-old startup, but still. ~6k lines of real terraform code. self-validated by making sure `pulumi preview` is clean. if you give opus the tools to verify its outputs, its unstoppable
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big supporter of memories not being locked up in provider silos. we've been through this spiel in all the past hype cycles.
The new Anthropic managed agents API is basically the Letta API that we've had since a year ago, but closed source and with provider lock-in. They even have read-only memory blocks and memory block sharing -- something which was unique to the Letta agents for a long time. Funny enough, we actually don't think this is the direction agents are going to go. Having API interfaces for memory blocks and tools is certainly convenient - you can spin up stateful agents as API services with just a few lines of code. But its also limiting: LLMs today are extremely adept at computer-use, and representing their memories in this way limits the action space of agents and their ability to learn. It's important to remember that just because something comes out of a frontier lab, doesn't mean its the "right" answer long-term. The Letta API ~1 year ago was somewhat of an antipattern in a sea of agent framework libraries offered by every lab. But now, stateful agent APIs are becoming the new norm - especially as providers try to lock in memory/state into their platforms to increase switching costs (which is exactly why we believe memory should live outside of model providers) If you want to see what the future is going to look like, follow @Letta_AI
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talking to a gpt-5.4 openclaw feels like talking to a 10 year old compared to Opus I have to spell out everything. it never extrapolates the obvious implications of what I say. I have to wonder if this is intentional, tuning to improve instruction following to the T?
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