Every company can rent the same frontier models. So the edge is AI trained on your own data. FLock's answer is private AI that runs inside your walls. Before an enterprise trusts AI with sensitive work, it needs 4 checkboxes: 1. Where it runs: On-premises, at the edge, in a private cloud, or fully air-gapped. Your data never leaves your environment. 2. What it learns from: Your own data, through fine-tuning and federated learning. Models train across data silos. The raw data stays where it is. 3. Who sees what: Fine-grained access control. Each user only reaches the data they're cleared for. 4. Whether it's compliant: GDPR, HIPAA and EU AI Act ready by design. We worked with NHS trusts and UCLH to train a glucose monitoring alerts model together. Cross-site predictions improved. Not one patient record moved. And it's not just healthcare. Biotech, pharma, law firms, banking and finance, crypto, defense, public services and other regulated industries need private AI too. Enterprises can keep ownership of the model, the data and the controls. That's how your company should adopt AI too.

AI-led attacks usually don’t go after your money right away. They read your logs, wait, and take down the whole system. Our CEO and founder, @0x7SUN, on data security in the agentic world and why companies should build their own AI. Full episode on Bae HQ. Out this Friday.

Governments want AI in public services, but citizen data is highly sensitive and tightly regulated. FLock helps public bodies build and deploy their own AI, keep raw data local, and share only what the model learns. Our Head of EMEA, @TiffanyWang98, shares our pilot-to-scale framework for deploying AI safely and securely on @techUK’s Digital Transformation in the Public Sector Focus Series.

AI is running out of real data. Synthetic data is the backup plan. But most of it has a logic problem. Labs now train models on data written by other AI. It's cheap, fast and private. However, there's a massive flaw. AI writes what sounds likely, not what's correct. It breaks basic rules. It repeats common examples and skips the rare ones. The next models learn those mistakes. CircuitSynth, from researchers at the Department of Computer Science @UniofOxford, @flock_io, and the Institute of Logic and Computation, TU Wien, splits the job in two. First, a small logic system picks the facts for each example. It can't pick combos that break the rules. A "pregnant male patient" is impossible by design. Then the AI turns those facts into sentences. It can't add facts of its own. The test was hard logic puzzles. @AIatMeta's Llama 3.1 8B followed all the rules 12.4% of the time. CircuitSynth hit 100%. It also covered far more rare cases, 52% against 35% for the next best method. That's the gap between messy data and data you can train on. The catch is that it struggles when the rules get very complex. Clean rules in. Clean model out.

99% of companies use AI like Google: → ask a question → get the same answer as everyone else The 1%: → train on their own data → own the model → get their Grok moment Grok had X's data. You have yours. @flock_io

Governments want AI in public services, but citizen data is highly sensitive and tightly regulated. FLock helps public bodies build and deploy their own AI, keep raw data local, and share only what the model learns. Our Head of EMEA, @TiffanyWang98, shares our pilot-to-scale framework for deploying AI safely and securely on @techUK’s Digital Transformation in the Public Sector Focus Series.

Enterprises don’t need the biggest model. What they need is the one that knows their work. Small models that can run on edge devices, trained on the organisation’s own data for specific tasks. 5 reads on how custom models are earning their place in regulated industries👇

One hacked system took down fridges across hundreds of French supermarkets. That’s the risk of centralisation. AI is following the same path. Our founder & CEO @0x7SUN on why AI needs to be private, and why FLock on Bae HQ. Out Friday.

Enterprises don’t need the biggest model. What they need is the one that knows their work. Small models that can run on edge devices, trained on the organisation’s own data for specific tasks. 5 reads on how custom models are earning their place in regulated industries👇

Local vs. local decision models. One bay warehouse rule, reworded 11 ways. “A bay is eligible only if its handling is frozen.” “Bays without frozen handling are ineligible.” THIS/THAT 1.2 picked the right bay 93% of the time. Laya: 13%. Somewhere, ice cream is melting.

Our CEO @0x7SUN told Korea IT Times “For many critical enterprise tasks, generating open-ended text is an unnecessary cost and performance bottleneck.” “Specialized 2B-parameter models running locally can outperform massive cloud LLMs in speed, accuracy, and cost-efficiency.”

Proud for @DCGco to have backed @flock_io early - Qwen just spotlighted their open-source THIS/THAT model: 87.8% on complex decision tasks (up from 40.6% in v1.0), beating GPT-5.6 and Claude Opus 5, running locally in ~31ms. Built on Qwen3.5, trained on @alibaba_cloud . 🚀

Why does AI training even need blockchain? What are the real use cases for privacy-preserving and decentralised AI? Our Head of EMEA, @TiffanyWang98, takes on both questions, and more on the convergence of AI and blockchain with @0xmauricio on @blockdropspod. Out today 🎙️