▲ 14 r/claude
Honey, why don't you come downstairs and show everyone what you built with Claude Fable 5
Drop what you have been building with Claude Fable 5 in the comments.
u/prasadpilla — 3 days ago
u/prasadpilla
r/claude r/EngineeringManagers r/softwarefactories r/hermesagent r/AI_Coders r/ClaudeCode r/Btechtards r/theprimeagen r/microsaas r/cursor r/SaaS r/softwarearchitecture r/SaasDevelopers
Honey, why don't you come downstairs and show everyone what you built with Claude Fable 5
Drop what you have been building with Claude Fable 5 in the comments.
u/Gil_berth — 1 day ago
▲ 2 r/SaaS
We shipped 270 tasks in 7 days with just 3 engineers.
This is what our Agentic Engineering loop looks like.
270 tasks in 1 week with just 3 engineers. Many of these tasks are fairly complex.
My traditional engineering mind would've never imagined we could move at this pace.
I've managed teams where 10 engineers would complete 20–30 tasks in a week and still have plenty of spillover. Today we're doing roughly 10x that, and we're just getting started.
The biggest shift is that our job is no longer completing tasks it's building the loop that completes tasks.
Right now, 70%+ of our engineering tasks are completed mostly autonomously. Our goal is to push that to 95% while doubling the number of tasks the system can process.
For SaaS teams, this isn't about replacing engineers. It's about shipping faster without scaling headcount at the same pace. The bottleneck shifts from execution to designing systems that let humans and AI work together effectively.
It's systems engineering at the end of the day, with agents and humans as components in the pipeline.
Happy to answer anything about our stack, workflows, orchestration, review process, failures, costs, metrics, or how we got here. If it helps your team ship faster, I'm happy to share everything we've learned.
u/prasadpilla — 3 days ago
▲ 80 r/cursor
so $200/week is the new cap?
if they actually did this, then it is likely verifiably true that a dollar above $200/week is waste.
u/prasadpilla — 3 days ago
We shipped 270 tasks in 7 days with just 3 engineers.
This is what real Agentic Engineering Loop looks like!
270 tasks in 1 week, just 3 engineers. Many of these tasks are very complex.
My traditional engineering mind would never have imagined the scale at which we're moving now.
I managed teams where 10 engineers would get 20-30 tasks done and still have a lot of spill over. We are now able to do literally 10x of that, and we are just getting started.
The responsibility of the team now is moving from doing the tasks to building the loop that gets the tasks done. There has been a big shift even for us, a team who has been at the edge of Agentic Engineering.
We are currently at a place where 70%+ of our tasks get done mostly autonomously, we aim to push that to 95% soon and also double the number of tasks the system can process.
It's systems engineering at the end, but with agents and humans as components in the pipeline.
Ask me anything about our stack, workflows, orchestration, review process, failures, costs, metrics, or how we got here. If it's useful to other engineers, I'll answer as openly as I can.
u/prasadpilla — 3 days ago
We shipped 270 tasks in 7 days with just 3 engineers.
This is what real Agentic Engineering Loop looks like!
270 tasks in 1 week, just 3 engineers. Many of these tasks are very complex.
My traditional engineering mind would never have imagined the scale at which we're moving now.
I managed teams where 10 engineers would get 20-30 tasks done and still have a lot of spill over. We are now able to do literally 10x of that, and we are just getting started.
The responsibility of the team now is moving from doing the tasks to building the loop that gets the tasks done. There has been a big shift even for us, a team who has been at the edge of Agentic Engineering.
We are currently at a place where 70%+ of our tasks get done mostly autonomously, we aim to push that to 95% soon and also double the number of tasks the system can process.
It's systems engineering at the end, but with agents and humans as components in the pipeline.
Ask me anything about our stack, workflows, orchestration, review process, failures, costs, metrics, or how we got here. If it's useful to other engineers, I'll answer as openly as I can.
u/prasadpilla — 3 days ago
We shipped 270 tasks in 7 days with just 3 engineers.
This is what real Agentic Engineering Loop looks like!
270 tasks in 1 week, just 3 engineers. Many of these tasks are very complex.
My traditional engineering mind would never have imagined the scale at which we're moving now.
I managed teams where 10 engineers would get 20-30 tasks done and still have a lot of spill over. We are now able to do literally 10x of that, and we are just getting started.
The responsibility of the team now is moving from doing the tasks to building the loop that gets the tasks done. There has been a big shift even for us, a team who has been at the edge of Agentic Engineering.
We are currently at a place where 70%+ of our tasks get done mostly autonomously, we aim to push that to 95% soon and also double the number of tasks the system can process.
It's systems engineering at the end, but with agents and humans as components in the pipeline.
Ask me anything about our stack, workflows, orchestration, review process, failures, costs, metrics, or how we got here. If it's useful to other engineers, I'll answer as openly as I can.
u/prasadpilla — 4 days ago
▲ 14 r/hermesagent
A Mixture of Agents That Costs ~$0.15 per Turn
someone just built a really interesting Mixture of Agents (MoA) setup.
instead of relying on a single frontier model, every prompt is fanned out to multiple models (gpt-5.5, deepseek-v4-pro, sonnet-4.6), and then opus-4.8 acts as the aggregator that synthesizes the final answer.
the surprising part isn't the architecture, it's the economics.
a single opus call costs around $0.14.
the full 5-model ensemble costs around $0.15.
because most of the token cost comes from the shared system prompt/tool schemas, the additional reference models run on stripped-down context and add almost nothing to the bill.
according to HermesBench, this ensemble also significantly outperforms the individual models.
feels like we're moving from asking "which model should i use?" to "what's the best team of models for this task?"
really clever engineering.
u/prasadpilla — 8 days ago
▲ 32 r/hermesagent
Mixture of Agents
Hermes Agent just shipped the most interesting feature I've seen in a while. It combines multiple AI models into one answer that beats any of them alone.
It's called Mixture of Agents. Instead of sending your task to one model, it runs two or three frontier models in parallel and has an aggregator combine them into the final answer. One response, backed by several models cross-checking each other.
Nous says it scores 8% higher than Opus 4.8 and 11% higher than GPT 5.5 on their own upcoming benchmark. You pick it from the model dropdown like any model, and toggle it mid-task with /moa.
u/prasadpilla — 8 days ago
▲ 13 r/AI_Coders
Agentic Engineering Is Mostly Vibe Coding With Better Marketing ?!?
I think this whole vibe coding vs Agentic Engineering debate is arguing about the wrong thing.
people keep asking whether ai will replace engineers, but i think they're mixing up writing code with engineering.
To me there are two layers to engineering.
first is figuring out what should exist. breaking down an ambiguous problem, choosing the right abstractions, designing a system that will still make sense a year from now, deciding where responsibilities live and how everything fits together.
Second is turning that design into code.
agents have become incredibly good at the second part. they're getting faster every month and i expect that trend to continue.
But the first part is still where software succeeds or dies.
bad architecture doesn't become good because it was generated faster.
a codebase with the wrong abstractions is still a bad codebase whether a human or an llm wrote it.
this also isn't the first time we've gone through a shift like this.
we used to write machine code. then assembly. then C. then python and javascript. every generation raised the level of abstraction. nobody argued that python killed engineering because we stopped writing binary.
AI feels like the next abstraction layer.
Instead of spending most of our time expressing ideas in syntax, we'll spend more time expressing them as systems.
that's why i don't think engineering is disappearing. The implementation layer is becoming cheaper while the architecture layer becomes even more valuable.
models are already getting better at avoiding duplicate code, finding existing abstractions and following project conventions. they'll keep improving.
But serious software isn't difficult because writing functions is difficult.
it's difficult because every decision affects ten other decisions.
humans still think about business constraints, future roadmap, technical debt, tradeoffs and the entire system at once.
Agents are still mostly optimizing a local part of the graph. they don't really have the holistic view.
maybe that changes one day.
But today i think good engineering is simply moving up another level of abstraction, not disappearing.
Curious where people disagree.
u/prasadpilla — 8 days ago
Agentic Engineering Is Mostly Vibe Coding With Better Marketing ?!?
I think this whole vibe coding vs Agentic Engineering debate is arguing about the wrong thing.
people keep asking whether ai will replace engineers, but i think they're mixing up writing code with engineering.
To me there are two layers to engineering.
first is figuring out what should exist. breaking down an ambiguous problem, choosing the right abstractions, designing a system that will still make sense a year from now, deciding where responsibilities live and how everything fits together.
Second is turning that design into code.
agents have become incredibly good at the second part. they're getting faster every month and i expect that trend to continue.
But the first part is still where software succeeds or dies.
bad architecture doesn't become good because it was generated faster.
a codebase with the wrong abstractions is still a bad codebase whether a human or an llm wrote it.
this also isn't the first time we've gone through a shift like this.
we used to write machine code. then assembly. then C. then python and javascript. every generation raised the level of abstraction. nobody argued that python killed engineering because we stopped writing binary.
AI feels like the next abstraction layer.
Instead of spending most of our time expressing ideas in syntax, we'll spend more time expressing them as systems.
that's why i don't think engineering is disappearing. The implementation layer is becoming cheaper while the architecture layer becomes even more valuable.
models are already getting better at avoiding duplicate code, finding existing abstractions and following project conventions. they'll keep improving.
But serious software isn't difficult because writing functions is difficult.
it's difficult because every decision affects ten other decisions.
humans still think about business constraints, future roadmap, technical debt, tradeoffs and the entire system at once.
Agents are still mostly optimizing a local part of the graph. they don't really have the holistic view.
maybe that changes one day.
But today i think good engineering is simply moving up another level of abstraction, not disappearing.
u/prasadpilla — 8 days ago