Roanoke AI & Automation

Marketing + Ops + Automation Agency | @MITxPro Certified Designing & Building AI Product & Service | @JMUsmad Alum. Personal ⤳ @ChrisFunyons

[Roanoke AI & Automation @RoanokeAI]

15/15 Verified fixes are the currency.

Keep the receipt: what changed, what passed, and what still needs a person.

Plugin: https://t.co/W88kcn6Eqd
CLI: https://t.co/keH1eUJlGg
Session: https://t.co/8xUKCtHkHZ

[Roanoke AI & Automation @RoanokeAI]

14/15 Put the fine print in the plan:

• CLI and SDK are public; access and hosting vary.
• Keep unrelated credentials out of scans.
• Reports may contain code and vulnerability details.
• Deployed systems still need their own testing.

[Roanoke AI & Automation @RoanokeAI]

13/15 Our recommended pilot:

1. One critical repository you own, with a named reviewer.
2. A focused fix: reproduce, patch, test.
3. An advisory scan: measure signal quality, cost, and delay.
4. An enforced gate: expand when the workflow earns it.

[Roanoke AI & Automation @RoanokeAI]

1/15 The scanner found it. Now show the fix works.

Our takeaways from OpenAI's Daybreak Live: validate the finding, test the patch, and put the decision in human hands.

A 15-slide guide to the evidence between a finding and a reviewed fix.

[Roanoke AI & Automation @RoanokeAI]

12/15 Pick the model. Pick the effort. Watch the dollars.

An estimated scan budget is not a hard spending cap. Requests already in progress can finish above it.

Choose settings against the coverage and turnaround time your team needs.

[Roanoke AI & Automation @RoanokeAI]

11/15 Then it waited for a human.

In the demonstrated workflow, the agent opened the pull request and notified the engineer in Slack.

The engineer supplies context, sets the threshold, and reviews the diff. The handoff includes the evidence.

[Roanoke AI & Automation @RoanokeAI]

10/15 Repository rules make the gate real.

A finding above the configured severity threshold fails the scan check. To block a merge, the repository must REQUIRE that check.

Annotations alone do not enforce the gate.

[Roanoke AI & Automation @RoanokeAI]

9/15 From one repository to every pull request.

The demo showed 1 repository in the desktop app and 2 selected from 32 listed. Pull requests can be checked through configured CI.

Evaluate signal quality, runtime, and cost before enforcing a gate.

[Roanoke AI & Automation @RoanokeAI]

8/15 An unfinished scan is unfinished work.

Exit code 2 means the scan hit an error or left coverage incomplete.

Review partial or unknown coverage. Check deferred areas before relying on the result. A finished command is not always a complete review.

[Roanoke AI & Automation @RoanokeAI]

7/15 The patch ships with its own receipt.

1. What changed: a focused patch.
2. What was tested: before-and-after results.
3. What remains: coverage limits and proof gaps.

Passing tests are evidence. The engineer still makes the merge decision.

[Roanoke AI & Automation @RoanokeAI]

6/15 Evidence before urgency.

Look for an attack path, a source-to-sink trace, and a severity assessment of reachable impact.

The Ladybird demo involved about 28,000 files, per the session notes. The size matters less than evidence a reviewer can inspect.

[Roanoke AI & Automation @RoanokeAI]

5/15 Give the scanner the context it needs.

SECURITY.md should explain:
• Scope: what belongs in this review?
• Existing controls: what already limits risk?
• Business logic: which rules must the system uphold?

Useful findings start with clear boundaries.

[Roanoke AI & Automation @RoanokeAI]

4/15 The work between the work:

Context → Find → Validate → Fix → Verify → Human review.

The reviewer decides what ships. That decision feeds context into the next cycle.

Automation moves the work. A person owns the decision.

[Roanoke AI & Automation @RoanokeAI]

3/15 Two questions deserve separate evidence:

Is it real? Trace a reachable attack path.

Is it fixed? Retest the issue and record the gaps.

False positives waste review time. Regressions create more work.

[Roanoke AI & Automation @RoanokeAI]

2/15 OpenAI reported 30M+ commits scanned across 30,000+ codebases, with 500,000+ findings automatically determined to be fixed.

Those June 22, 2026 figures do not establish how many AI-written patches reached production. Read the definition with the number.

[Roanoke AI & Automation @RoanokeAI]

6x growth YoY for @Airbnb stopped chasing tourists & started selling to locals. Paris is converting local residents into bookers, a segment rival platforms have written off as unprofitable. @Roanoke_Region gig economy level programming. Let’s build!

https://t.co/ZpCf6Akzit

[Roanoke AI & Automation @RoanokeAI]

Business owners in the Roanoke Valley, Blacksburg & New River Valley: I’m building an AI readiness curriculum for small and midsize businesses.

Which topic would help you most? Vote, then reply with one task you’d like AI to help with.

[Roanoke AI & Automation @RoanokeAI]

Every agent needs a job.
Someone needs the whole outcome.

Check the result against the original goal, and its limits.

https://t.co/HtqGmlg912

[Roanoke AI & Automation @RoanokeAI]

Turn the dial before you switch the model.

Claude Opus 5.5 has five effort levels that control how much thinking it puts into a task.

Results falling short? Give it a way to check its work, then try more effort before switching to a more powerful model.

[AI_Explorer @ai_explorer25]

list of 15 best AI researchers on X:

1. @karpathy — Andrej Karpathy
2. @rasbt — Sebastian Raschka
3. @AndrewYNg — Andrew Ng
4. @drfeifei — Fei-Fei Li
5. @demishassabis — Demis Hassabis
6. @GaryMarcus — Gary Marcus
7. @ai_explorer25 — AI Explorer
8. @maximelabonne — Maxime Labonne
9. @chipro — Chip Huyen
10. @ilyasut — Ilya Sutskever
11. @thsottiaux — Thibault "Tibo" Sottiaux
12. @bcherny — Boris Cherny
13. @geoffreyhinton — Geoffrey Hinton
14. @JeffDean — Jeff Dean,
15. @fchollet — François Chollet