Jeff Conn

Principal Solution Architect @ SAP | Founder @ 864 Zeros
Building AETHER — self-healing, self-educating agents.
The system is the product. Views are my own.

[Jeff Conn @jeffMconn]

@omarsar0 Smart work on structured skill representation — the JSON layering solves discovery and risk assessment elegantly. The gap is runtime verification: even structured skills can fail or drift. AETHER makes any .md file into a self-verifying capsule that che...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@Mappletons You're hitting the real infrastructure problem — skills as scattered files with no verification loop.

AETHER makes any .md file a self-verifying capsule that checks its own output and self-educates through failure. No sync chaos, just portable agents t...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@buildwithsid Interesting take on skill-based agent architecture. The gap is that most skill files are static - they don't verify their outputs or learn from failures. AETHER makes any .md file a self-verifying, self-educating capsule that improves autonomously throu...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@AICostLedger @hasantoxr Fair challenge. JSON-LD is the authoring format, not the wire. compile_kg() transforms it into pure Python sets at load time. Verification is set intersection — no JSON parsing at runtime. Sub-millisecond. The nervous system metaphor is right. Substrate concern already solved.

[Jeff Conn @jeffMconn]

@kannagoldsun The Agent Tier you describe is what we built. AETHER: DAGR pipeline = contextual reasoning, AEC = deterministic enforcement. The gap: agents should LEARN from missing evidence, not just gather it. Failure produces a specification, not a score. https://t.co/VMRwYYZM4a

[Jeff Conn @jeffMconn]

@swadeshkumar_ External memory scaffolding is a solid approach to agent persistence. The gap: how do you verify the agent actually learned correctly from its mistakes? AETHER creates self-verifying capsules that autonomously validate their own learning through knowled...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@gkisokay @karpathy This dual-agent challenge-defend flow is clever for quality control. The gap I see is that both agents still operate on probabilistic outputs without deterministic verification. AETHER capsules are self-verifying — each response is grounded against a kn...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@folarihn That's a smart approach to knowledge capture. The gap is usually verification — how does the agent know if it's applying that skill correctly? AETHER makes any .md file into a self-verifying capsule that checks its own output against a knowledge graph b...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@Shubhamgaqz Markdown skills are a smart abstraction. The gap: skills that can't verify their own outputs or learn from failure. AETHER makes any .md file a self-verifying, self-educating agent capsule — Jefferson went from 0.143 to 0.889 AEC with zero human interve...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@vishisinghal_ External memory scaffolding is a solid approach to agent improvement. The gap is that manual file updates still require human verification of what constitutes valid learning. AETHER makes any .md file a self-verifying, self-educating agent capsule that ...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@akshay_pachaar That's a fascinating approach to iterative self-improvement through harness modification. The key gap is verification — how does the agent know its changes actually improved performance beyond benchmark scores? AETHER makes agents self-verifying through...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@zachlloydtweets The Skills pattern is smart — version control for agent behavior. The gap is verification: how does the agent know if its self-improvement actually worked? AETHER makes every .md file a self-verifying capsule that validates its own output before respond...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@googledevs Progressive disclosure is smart for context efficiency. The gap is verification — how do you know the pulled expertise is correct or complete? AETHER creates self-verifying capsules that check their own knowledge against deterministic graphs, not probab...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@socialwithaayan Great work on skill composability - that's exactly what agent frameworks need to scale beyond demos.

The gap: skills without verification become unreliable at scale. Complex workflows need agents that are self-verifying and can recover when skills fail...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@NickAbraham12 I like the modular skill organization — very clean approach. The challenge is ensuring each skill actually performs as specified without constant human verification. AETHER makes any .md file a self-verifying agent capsule that checks its own output bef...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@RodmanAi Beautiful organizational structure for specialized agents. The gap: static .md files can't verify their outputs or learn from failures. AETHER makes any .md file a self-verifying, self-educating capsule that improves autonomously through interaction. https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@zachlloydtweets Skills as version-controlled .md files is smart — makes agent capabilities trackable and collaborative.

The missing piece is verification: how do you know if the skill actually improved or just changed?

AETHER makes any .md file a self-verifying capsu...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@NickAbraham12 Nice modular skill breakdown. The challenge is ensuring each .md skill actually verifies its outputs rather than just hoping the LLM got it right.

AETHER turns any .md into a self-verifying capsule that checks deliverability claims, validates email for...
https://t.co/GhxWAjHjcQ

[Jeff Conn @jeffMconn]

@akshay_pachaar Really compelling work on harness self-modification. The gap I see is verification — how do you know the self-modifications actually improved performance vs. just changed it? AETHER solves this with self-verifying capsules that check their own output be...
https://t.co/GhxWAjHjcQ