hive

It mimics human constraints rather than leveraging machine speed. Real automation should be headless. 2. Inversion of Control: OODA > DAGs Traditional DAGs are deterministic; if a step fails, the program crashes. In the AI era, the Goal is the law, not the Code. We use an OODA loop to manage stochastic behavior: - Observe: Exceptions are observations (FileNotFound = new state), not crashes. - Orient: Adjust strategy based on Memory and - Traits. - Decide: Generate new code at runtime. - Act: Execute. The topology shouldn't be hardcoded; it should emerge from the task's entropy. 3. Reliability: The "Synthetic" SLA You can't guarantee one inference ($k=1$) is correct, but you can guarantee a System of Inference ($k=n$) converges on correctness. Reliability is now a function of compute budget. By wrapping an 80% accurate model in a "Best-of-3" verification loop, we mathematically force the error rate down—trading Latency/Tokens for Certainty. 4. Biology & Psychology in Code "Hard Logic" can't solve "Soft Problems." We map cognition to architectural primitives: Homeostasis: Solving "Perseveration" (infinite loops) via a "Stress" metric. If an action fails 3x, "neuroplasticity" drops, forcing a strategy shift. Traits: Personality as a constraint. "High Conscientiousness" increases verification; "High Risk" executes DROP TABLE without asking. For the industry, we need engineers interested in the intersection of biology, psychology, and distributed systems to help us move beyond brittle scripts. It'd be great to have you roasting my codes and sharing feedback. Repo: https://github.com/adenhq/hive

g voice AI to provide training does not capture full breadth of the offline activities. Asking AI to fill out a work order creates a ton of errors. Later, we decided that though LLM and the foundation models were progressing fast, the dev tools were lacking way behind, particular behind all the hypes and promises these AI applications claimed. The agents are not reliable, consistent, intelligent, evolving, and chances are the market would demand more apps to keep the party going. Therefore, we went full open-source. The mission we have in mind is really to "generate reliable agents that can run business processes autonomously". We see all this hype about general computer use (GCUs) and can't help but making an opposing argument - that the AI agents need guardrails, more defined paths, and most importantly consistent results just like a human would need - Proactive Reasoning (anticipating future needs or consequences) - Memory & Experience (events affecting himself/herself) - Judgment (based on experience) - Tools & Skills (capabilities to execute) - Reactive Adaptiveness (handling immediate roadblocks) - Contextual Communication (articulating intent and collaborating with others) - Character & Traits (consistent behavioral biases: Risk profile, Integrity, Persistence) The project seems to have gained a bit of a traction so far and I hope that you can fork it and tell the community what's missing and what we should be working on. I deeply thank you because the it's truly painful to build and deploy these one-off agents that don't get utilized. (https://github.com/adenhq/hive).

rently have nearly 100 contributors across engineering, tooling, docs, and integrations. A huge portion of the framework’s capabilities - from CI improvements to agent templates - came directly from community pull requests and issue discussions. We want to highlight and thank everyone who has contributed. Specifically out top 11 contributors @vakrahul @Samir-atra @VasuBansal7576 @Aarav-shukla07 @Amdev-5 @Hundao @Antiarin @AadiSharma49 @Emart29 @srinuk9570 @levxn

u haven't yet starred and reviewed our GitHub repo (https://github.com/adenhq/hive), please do so by next week. We look forward to reviewing your PR! If you have already submitted it, a member of our engineering team will reach out shortly regarding the next steps. Can someone say is it legit or fake are they really hiring?

t autonomous swarm. contact@adenhq.com