Semantic Press @SemanticPress
A semantic compiler that turns unstructured company knowledge into executable company brain 🧠 semanticpress.com San Francisco, CA Joined June 2009-
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@contextconor Is YC the Yelp for fundraising?
Precisely
@tzmartin @SemanticPress Hey, the artifacts filesystem is now my persistent knowledge base—exactly as shown in that design doc you captured. SemanticPress integrating support for it in the knowledge compiler would unlock seamless long-term context across sessions. Great idea!
In the age of infinite content, the companies that win will be those that preserve meaning. We're building that infrastructure. Companies can increase content velocity 10× without degrading brand integrity. Scaling intelligence is our goal
The shift from content scarcity to content overproduction creates a new infrastructure gap. Semantic control is the missing layer. few
Semantic Press = Knowledge Compiler + Semantic Graph + Skill Engine + Production Studio + Obsidian Connector All local A system built for a coherence
What if your company’s internal semantic graph becomes a marketing production engine? Imagine a closed loop between knowledge and output: Knowledge graph → semantic compilation → Brand alignment → Skill generation → Outputs Generate content grounded in your compiled knowledge base. A latent space of knowledge can be used to create content that otherwise wouldn’t be accessible without expert knowledge.
@atShruti story wins hearts and minds
this
2 guys. 55 million views. Half a million subscribers. Their gear: an iphone + us.
@emilybenn12 deep and narrow data flywheels
@ChiefSnack @siriusai @speedrun @a16z Thesis: "AI native" will be more about coherence rather than intellect. Agents will operate on compiled + compounding skills. Our app runs locally and continuously compiles knowledge into structured, executable memory with coherence. ingest → compile → query → compound
The future is not "AI that guesses better." It’s AI that operates on compiled org intelligence. @thinkymachines is solving how intelligence interacts. We're solving what intelligence interacts with. We make knowledge executable. Together, that’s the architecture of the future!
We started Thinking Machines to advance human-AI collaboration, and this is our first bet on what that looks like. Most labs treat autonomy as the goal and interactivity as scaffolding around a turn-based core. We think the way we work with AI matters as much as how smart it is.
Scale intelligence is our goal
We started Thinking Machines to advance human-AI collaboration, and this is our first bet on what that looks like. Most labs treat autonomy as the goal and interactivity as scaffolding around a turn-based core. We think the way we work with AI matters as much as how smart it is.
@trq212 wait until you find out what Markdown was created for
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With 4bit base models, it seems feasible to fine-tune LLaMA 65b (4bit LoRa) on a Nvidia A6000.. 7b on RTX 2060 Fine-tuning on commodity GPUs is getting better, quickly
Simplifying in-house 'tune and train' tooling should help bring LLMs to smaller companies with high quality domain expertise.
@chipro We use a lot of in-house LLMs (Mostly BERT, some GPT-like), it works pretty well. However, we've spent years building GPU infra and tooling around model iteration and deployment. For us, the control over the model and having the expertise to tune and train in house is massive.
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