hffmnnj
2.3K posts

hffmnnj
@hffmnnj
Full-stack dev. Privacy nerd. Accidentally became a hardware founder @Pulsyn
Phoenix, AZ Katılım Mayıs 2018
418 Takip Edilen1.2K Takipçiler

@hffmnnj @ntbrown01 @haydendevs Apple is a close second to graphene and for hardware reasons. Even graphene devs will tell you this
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@ntbrown01 @haydendevs OP asked why but good reading comprehension redditor
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@hffmnnj @haydendevs The op asked about alternatives and you provided no suggestions. Zero value. Wasted elections.
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@ntbrown01 @haydendevs Then go use one of the plenty of Linux Phones that already exist with Android application wrapper layers
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@haydendevs And Linux by default is fundamentally insecure on a hardware level anyone can access your information with physical access.
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hffmnnj retweetledi
hffmnnj retweetledi

@joni_vrbt If it ships and it works you built it
The process is nobody's business but yours
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hffmnnj retweetledi

A future where plumbers get drafted NFL style sounds kind of fire not gonna lie
SAY CHEESE! 👄🧀@SaycheeseDGTL
Uber founder says AI will make human labor far more valuable, predicts plumbers could make “LeBron like money” in an automated world.
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@Polymarket I knew trump's new sites looked vibecoded
He's got Barron cranking 6 Claude maxes in the white house
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@huge_icons Claude models for frontend, taste-making or planning
GPT for complex backend or debugging
Kimi/GLM/Minimax for low level/fill in the blank tasks
OpenCode for the harness
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@contextkingceo Congrats on the raise!
Building a health tech product and the "similar ≠ relevant" problem is HUGE for medical data.
Curious if you see this tech as a use case?
And when can I start building with it?!
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We've raised $6.5M to kill vector databases.
Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest.
Similar, sure. Relevant? Almost never.
Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough.
A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file.
Once you’re dealing with 10M+ documents, these mix-ups happen all the time.
VectorDB accuracy goes to shit.
We built @hydra_db for exactly this.
HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time.
So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit.
Even when a vector DB's similarity score says 0.94.
More below ⬇️
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@DevvMandal @markov__ai This is huge for agent training. Congratulations!
What's the labeling/annotation strategy? Curious if you're capturing intent or just raw actions.
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Today, we're launching the world's largest open-source dataset of computer-use recordings.
10,000+ hours across Salesforce, Blender, Photoshop and more, to automate the next level of white-collar work.
Link in the comments :)
@markov__ai
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