Ryan Harty @HartyRyan

AI Engineer building agentic systems | Ex-quant, ex-VC ($1B AUM) | Now deploying AI at scale in energy & life science Joined November 2011

Tweets

534
Followers

248
Following

410
Likes

1K

Ryan Harty @HartyRyan

3 months ago

@trq212 The openclaw killer

0 0 0 17 0

View Details

Ryan Harty @HartyRyan

3 months ago

@trq212 Skills have revolutionized how my team works. Databases get turned into MCPS, SOPs get turned into skills. What took hours of manually work is done in minutes with cowork/Claude code.

0 0 0 159 0

View Details

Ryan Harty @HartyRyan

3 months ago

@cgtwts fire cheat sheet. Building custom mcps for data that is hard to connect to, then running custom skills for the automated workflows that require data, is the move

0 0 10 6K 5

View Details

@alexalbert__ The timing is wild. Getting blacklisted by the Pentagon turned into the best marketing week in AI history. Claude hits #1 on the App Store while the company that said yes to the DoW scrambles to rewrite their contract

0 1 1 217 0

View Details

Ryan Harty @HartyRyan

3 months ago

@GaryMarcus I see this in production constantly. Ask a model to review a strategy doc and it finds reasons to agree with whatever you wrote. The most dangerous AI failure isn't hallucination. It's confident confirmation of your worst ideas

0 0 0 37 0

View Details

Ryan Harty @HartyRyan

3 months ago

@emollick Coding benchmarks are easy because code has clear pass/fail. The hard enterprise work, summarizing messy contracts, extracting data from scanned PDFs, triaging ambiguous emails, has no clean eval. That's where most AI value actually lives.

0 0 1 87 0

View Details

Ryan Harty @HartyRyan

3 months ago

@bindureddy Leaderboard position means less every month. The real question is whether it holds up on the messy inputs enterprises actually send it. Benchmarks are clean rooms. Production is a construction site.

0 0 0 96 0

View Details

Ryan Harty @HartyRyan

3 months ago

@simonw @mempirate @AmpCode This is the whole game. An agent can write code all day but when it breaks at 2am, a human answers the page. Until agents can own consequences, they're tools not teammates

0 0 0 64 0

View Details

Ryan Harty @HartyRyan

3 months ago

@emollick Naming matters more than people admit. 'o3' sounds iterative. 'GPT-5' signals a generational leap. The capability jump was real but the branding buried it.

0 0 0 58 0

View Details

Ryan Harty @HartyRyan

3 months ago

@GaryMarcus This is the underrated risk. Everyone talks about hallucination. Sycophancy is worse because the user never realizes they're wrong. At least hallucinations are obviously false sometimes

0 0 0 34 0

View Details

Ryan Harty @HartyRyan

3 months ago

@svpino The skills/plugins/MCP naming shuffle is the real problem. Every update changes the abstraction layer. I stopped using framework features and just write plain .md context files. Boring but they don't break

1 0 1 401 0

View Details

Ryan Harty @HartyRyan

3 months ago

@simonw This is the real tension. Security teams want to gate access. Engineering teams already have 6 agents running in production. The gap between policy and reality grows every week

0 0 0 223 0

View Details

Ryan Harty @HartyRyan

3 months ago

@swyx @ankitxg The bottleneck was never the code review itself. It was the context transfer. Reviewer doesn't know why you made that choice. Agents have the same problem, they just skip the part where someone asks.

0 0 0 174 0

View Details

Ryan Harty @HartyRyan

3 months ago

@emollick The gap between leap 3 and leap 4 is where most enterprise value got created. Reasoners made AI useful for real work. Everything before that was demos

0 0 0 154 0

View Details

Ryan Harty @HartyRyan

3 months ago

@emollick This matches what I see in production. Models that ace benchmarks choke on messy real-world inputs. Corrupted PDFs, mixed-language docs, weird formatting. Robustness is the real moat, not leaderboard scores

0 0 1 532 0

View Details

Ryan Harty @HartyRyan

3 months ago

@GaryMarcus The moat was never the model. It's distribution and switching costs. When everyone copies the same play, the winner is whoever locked in enterprise contracts first. This is basic market structure

0 0 0 52 0

View Details

Ryan Harty @HartyRyan

3 months ago

@emollick The wild part is watching non-technical people debug their own prompts now. My content system has 16 scheduled agents and the hardest part wasn't

0 0 0 152 0

View Details

Ryan Harty @HartyRyan

3 months ago

@simonw Annotated prompts are underrated. Half the value of my agent system came from iterating on the context docs, not the code. The prompt IS the product at this point

0 0 0 175 0

View Details

Ryan Harty @HartyRyan

3 months ago

@svpino Missing the big one: give it your actual project context, not just rules. I have agents reading brain files, past analytics, and brand voice docs before writing a single line. The .md file is the new system prompt

0 0 1 645 1

View Details

Ryan Harty @HartyRyan

3 months ago

@emollick I built a content system that flags exactly these patterns. The irony is most "AI thought leaders" couldn't pass the same detector they're supposedly building with