What we are building, and what the data says when we point our own platform at a public dataset. Agentic data engineering, languages for data, model training, and case studies published with their numbers.
Agents that own a job instead of waiting for a prompt. Kin queries your databases, reads the web and your files, checks every number against its own query results, and asks before it changes anything. Free for 3 kin, in early access.
Anthropic publishes what people actually do with Claude, 1.6 million rows a month, free. Less than half of it is work. The busiest thing it makes is an explanation, not code. And the adoption gap between two American states is wider than the gap between America and India. Built on Bonacci Studio.
Every previous expansion was followed by a slide to the thinnest scoring in the competition's history. 2026 went to 48 teams and produced 2.96 goals a game, the highest since 1970. 104 matches, 308 goals, 1,248 players, and one pipeline that had to pull every goal out of the match it was buried in. Built on Bonacci Studio.
Run the same prompt twice, get two different answers. vibeCodeCLI races parallel fleets in your terminal instead, keeps memory across sessions, and checks the outcome before anything irreversible runs. Free, 23 providers, bring your own keys.
We rebuilt ThinkingMemory around one primitive: recall. Intent in, the right context out, hybrid-retrieved, ranked, and packed to a token budget, with an in-engine lifecycle, bitemporal audit, per-tenant RLS, and an MCP server. 100% recall@5 in roughly 68% fewer tokens than dumping the corpus.
GPT-4-class capability cost $37.50/M tokens in 2023. Today a 2B-parameter model serves the same benchmark score at $0.04. The frontier held its price; the efficient tier collapsed. The same open model spans a 13× price spread across 12 providers. Built on Bonacci Studio.
Four public datasets (BLS/OEWS, O*NET, Anthropic Economic Index, Indeed). Three occupation coding schemes. One crosswalk. AI has not cut jobs in aggregate, but it is bending the hiring curve along the automation line before headcount moves. Built in Bonacci Studio.
25 months of activity from GitHub, arXiv, and Hugging Face, 613 data points across five categories. RAG is up 340% year over year. Training from scratch is up 32% month over month. Nothing is cooling. The agent wrote the PySpark, ran it, and rendered the analysis live in Bonacci Studio.
A new research paper introducing AC-LSCM, an Action-Conditioned Latent Structural Causal Model that gives agents an explicit forward model with interventional semantics. 36× fewer safety violations than a Transformer baseline on synthetic planning tasks.
Three 9B Mixture-of-Experts models, 122B tokens each, pretrained on Google's TPU Research Cloud. The compute bill stayed small because of the research credits. The tokenizer, the corpus pipeline, and the eval harness were the work.
It lives inside Bonacci Studio, keeps its context in ThinkingMemory, and runs SQL, SSH, API calls, and MCP tools on its own. A heartbeat check watches your infrastructure and routes whatever breaks to Slack, Discord, Teams, email, or PagerDuty. You write the checks in TL or PySpark.
A compiled, statically typed language where tables, streams, tensors, and models are types the compiler knows about, rather than libraries bolted on. One language in place of the Python, SQL, Spark, and YAML you currently glue together.
One workspace with a terminal, a chat, and a codegen tab, where the agent holds real tool access. It explores the database itself, writes the ETL, and runs the PySpark. No copying output between windows.
The first version of our open-source memory layer: layered storage and reasoning-aware retrieval, so an agent stops solving the same problem twice. We rebuilt it around a single recall primitive five months later, in post 16.
A reader that cuts AI, tech, startup, market, and sport stories down to 60 words. Built because keeping ten tabs open to find one fact is not reading.
Silent data corruption usually starts with an upstream ALTER TABLE that nobody announced. Studio catches the drift, works out what changed, and proposes the fix before the bad rows land in the warehouse.
A walk through the Kafka Connect ecosystem inside Studio: the 16+ connectors we ship, how change data capture behaves in practice, and bi-directional flow for event-driven work.
Self-managed Linux boxes instead of the big clouds. 95% off the server bill, full control of the machines, and the operational work we took on in exchange.
The case studies above are real pipelines: the agent wrote the code, ran it on Spark, and rendered the results. Describe yours and watch it ship.