KevinSimback

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Imagine the possibilities
Inference capital markets x $ansem tie up
Leverage the attention to supercharge decentralized AI, agentic markets
cc @blknoiz06 and @0xgilbert
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I’ve been Fable-maxxing since it was reenabled
My subjective opinion - it acts with agency in ways no other models currently do
It does what I ask, then finds other important related stuff along the way and does that too
It takes care of the job like someone who cares
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While everyone is talking about Anthropic/OpenAI vs Chinese open source models
Just remember there are other players that can make some big announcements on any given day to shift the balance on either side
Prediction: we get a big one within the next 1-2 weeks
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Coding is the undeniable PMF use case for AI, but most knowledge work still has a ways to go
Coding works because all the context lives in a git repo that is versioned, structured, in one place, and usually with a test suite that tells you what is correct
Knowledge work on the other hand is based on information that resides in a bunch of different places - Slack, email, various systems, and often in people's heads
So if you want knowledge work to be automated like code, you need a "context repo" or as often called a "company brain"
But a company brain that just takes files, puts them somewhere
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Open source AI is having quite a moment, and I’m here for it
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Putting an LLM as judge is the simplest and easiest way to create a loop
1. Do the thing you already do by prompting AI
2. Use another model to evaluate the output and give it feedback
3. Iterate until both are satisfied
Only see the final output once the loop is complete
Since you're using an LLM as judge, this is generally best for non-deterministic tasks like research, design, building specs, etc.
Using Looper to do this will make it super easy by refining your prompt for a better constructed loop, adding the judge, and setting the exit criteria so it doesn't run unnecessary iterations
Now
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I love the recent debates on memory, but here’s what many are missing:
In the span of ~6 months we’ve gone from mostly general chat to heavy agentic usage that is 5-100x+more memory intensive
And it won’t stop there
The demand side of this equation is insane
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SAHEN:
new update the go to the moon 🌚

my UID a good day today Love The ideas for a better life is a redpack the ideas I
PTSD induced, iykyk
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GLM 5.2 vs Opus 4.8 vs GPT 5.5
At Delphi we are power users of AI and have a very active group chat to discuss all the models and trends
General consensus:
> All 3 have their good and bad moments, no decisive winner
> GLM is quite good as many on the timeline suggest
> Opus still best at design and visualizations
> GPT is the most common workhorse model for everyday stuff + agents
Having all 3 via subsidized coding plans is the power play
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Just departing Italy amidst this heat wave, I really don’t get why AC is even a debate
Literally everyone - locals included - are complaining
Sweltering indoors just makes people irritable, install the AC already
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If you’re not US or China, I don’t know how you sit back and become dependents on them for the intelligence that will define the future
You’d think it would be a national priority to have a strategy here
Of course, easier said than done - you need big $ and talent to compete with the frontier labs
The easiest first step is to build sovereign data centers and host the open weights models or post-trained versions - at least you own some form of the intelligence
The UAE and Saudi Arabia seems to be going this path and making moves towards training
The most impactful next step would be focusing on
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If you’re doing diligence on a new startup, you now need to assess their “AI nativeness” alongside team, product and market
Why? If they’re not at the forefront of using AI, it puts them at risk of not executing fast enough
It’s not a hard pass filter, but it is signal - here’s a few early tells:
1. Did they give you a .md file, alongside a deck, that you can feed to your LLM?
2. Did they build an interactive HTML deck or just send a PDF/docsend?
3. Do they talk about proprietary skills, data, evals or methods they’re using to build their product?
4. Do they ship significant product updates be
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If you’re diligence a new startup, you now need to assess their “AI nativeness” alongside team, product and market
Why? If they’re not at the forefront of using AI, it puts them at risk of not executing fast enough
It’s not a hard pass filter, but it is signal - here’s a few early tells:
1. Did they give you a .md file, alongside a deck, that you can feed to your LLM?
2. Did they build an interactive HTML deck or just send a PDF/docsend?
3. Do they talk about proprietary skills, data, evals or methods they’re using to build their product?
4. Do they ship significant product updates between mee
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Riddle me this:
What happens when GLM-6 comes out and has Fable/Mythos capabilities?
It’s not a matter of if, it’s when
And understanding the scenarios and implications are incredibly important
GLM3.73%
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Just signed up for a Sakana Fugu sub plan - let’s see how fast I burn it up
Hard to not try it out with these scores
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A big problem with crypto right now is you have to assume every new token project is going to rug, either intentionally or not
But for decentralized AI to work, you need some coordination mechanisms and tokens make most sense
How do we reconcile this?
TOKEN3.28%
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B200 at $4.37/hr - I’d be a buyer here
The drop in H100s make sense - those just aren’t as good for large models or agentic workloads with heavy cache
B200 however is a powerhouse, that’s where you’d want to run GLM-5.2 and the SOTA open source models
I’d go long that demand
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