The Survival of the Smartest: Exploring Darwinian Software Development
History / Edit / PDF / EPUB / BIB / 3 min read (~536 words)Write an article about Darwinian software development, the use of LLM swarms to identify the best solution to a problem.
In the traditional world of coding, software is "built." An architect designs a blueprint, and developers lay the bricks. But in the era of Generative AI, we are moving toward a paradigm where software is evolved.
Darwinian Software Development is an emerging methodology that leverages "swarms" of Large Language Models (LLMs) to iterate, compete, and select the most efficient code through a process mirroring natural selection.
Instead of asking a single AI to write a function, Darwinian development deploys a multi-agent system—a swarm—to tackle the same problem from different angles. This process generally follows four biological phases:
- Variation (The Mutation): Multiple LLM agents are given the same prompt but with different "temperaments" or system instructions. One might prioritize memory efficiency, another readability, and a third raw speed.
- Competition (The Struggle for Life): The agents generate dozens of potential solutions. These aren't just checked for syntax; they are put into a "sandbox" to run against unit tests and edge cases.
- Selection (Fitness Function): An automated "Judge" agent (or a specialized testing suite) evaluates the outputs. Only the code that passes the most tests with the lowest latency or resource consumption "survives."
- Heredity (Refinement): The winning code is then used as the "DNA" for the next generation. The swarm takes the best-performing snippet and attempts to optimize it further until a "perfect" solution is reached.
The "Darwinian" approach solves the two biggest headaches in AI-assisted coding: hallucinations and tunnel vision.
- Error Correction: If one agent introduces a bug, the likelihood that five other agents will make the exact same error is low. The swarm acts as a self-healing mechanism.
- Diverse Architectures: One LLM might get stuck on a specific logic path. A swarm explores the entire "fitness landscape," finding creative solutions a human (or a single AI) might never consider.
- Performance Benchmarking: By racing agents against each other, the system naturally optimizes for performance.
| Feature | Traditional Development | Single LLM Coding | Darwinian Swarm |
|---|---|---|---|
| Speed | Slow (Human-paced) | Very Fast | Fast (Iterative) |
| Reliability | High (Human-vetted) | Medium (Hallucination risk) | Very High (Cross-vetted) |
| Optimization | Manual | Basic | Automated/Evolutionary |
The biggest challenge in Darwinian development isn't the AI—it's the criteria. For natural selection to work, the environment must be rigorous. If your "fitness function" (the tests you use to judge the code) is poorly written, the AI will evolve to pass the test while still being technically broken. This is known as reward hacking.
To counter this, modern swarms often include "Adversarial Agents" whose only job is to write difficult unit tests to try and "kill" the code generated by the other agents.
We are approaching a point where software won't be static. Imagine a server that monitors its own performance and, upon detecting a bottleneck, spawns a Darwinian swarm to rewrite its own inefficient modules in real-time.
In this new world, the developer's role shifts from writer to naturalist. You aren't typing the lines; you are defining the environment in which the best code can grow.
What Changes When Coding Agents Are Infinitely Parallel
History / Edit / PDF / EPUB / BIB / 3 min read (~514 words)Imagine you can run hundreds/thousands of coding agents in parallel.
How would you go about using them?
This is one of those questions where the interesting answer isn't "do everything faster" but rather what fundamentally changes when parallelism is cheap.
The core shift: from sequential exploration to parallel search
When you have one agent, you think carefully before acting — it's expensive to go down dead ends. With thousands, the calculus flips: exploration becomes cheap, convergence becomes the hard problem. Your job shifts from "what should I try?" to "how do I synthesize thousands of results?"
High-leverage use patterns
Speculative execution on decisions — At every architectural fork ("should this be a queue-based system or polling?"), you don't decide, you branch. Two fleets of agents build both, and you evaluate the result. This is huge for situations where you genuinely don't know which approach is better until you've tried it.
Mutation testing at scale — Spin up hundreds of agents making targeted changes to a codebase, each trying a different hypothesis about why a bug exists or how to optimize something. Like fuzzing, but semantically directed.
Full-stack consistency checking — Have agents simultaneously hold the contract between every pair of services in your system, constantly verifying that implementations match specs, that error handling is symmetric, that naming is consistent. Things that fall through the cracks in sequential review.
Competitive benchmarking of approaches — For a problem like network latency or caching strategy, you could have 50 agents implement 50 different approaches against the same test harness, and just... pick the winner. No reading papers and reasoning about tradeoffs — empirically settle it.
Living documentation — Agents continuously reconcile docs, comments, and code. Every PR triggers agents that check for documentation drift, update runbooks, and surface inconsistencies.
The harder problems this creates
Synthesis is the bottleneck. If 1000 agents each produce a PR, you're back to being the serial bottleneck reviewing them. You need meta-agents whose job is to evaluate and rank the output of other agents, with clear scoring functions.
State and conflict. Agents working in parallel on the same codebase will conflict. You probably want them working in isolated sandboxes (branches, ephemeral clusters, test environments) with a merge/tournament layer on top.
Task decomposition quality matters more, not less. Bad task specs mean 1000 agents going confidently in the wrong direction simultaneously. The skill of writing tight, evaluable task specs becomes enormously valuable.
Evaluation functions become load-bearing. "Did the agent succeed?" needs a concrete, automated answer — you can't eyeball 1000 outputs. This pushes you toward test-driven development in a serious way.
What this implies for infrastructure work
The most compelling application is parallel experimentation on real infrastructure parameters. Spin up ephemeral environments, have agents try different configurations or scheduling strategies, measure actual performance, tear them down. The thing that makes infrastructure hard is that you normally can't afford to run 50 experiments simultaneously. With cheap parallel agents managing the scaffolding, that constraint disappears.
The practical limit ends up being compute and money, not ideas — which is a fundamentally different world than the one most engineering workflows were designed for.
- CometML
- ML model profiling and optimization
- GCP GCS performance profiling
- NVIDIA MPS
- NVIDIA MIG
- NVIDIA time-slicing
- NVIDIA vGPU
- NVIDIA KAI scheduler
- NVIDIA run:ai
Every week at the beginning of the (work) week.
5 days.
- Review and correct LLM generated summaries of last week activities
- Weekly planning
Every daily at the beginning of the (work) day.
8h.
- Review and correct LLM generated summaries of yesterday activities
- Catch up on slack messages (manual)
- Review PRs (manual)
- Standup (manual)
- Go to meetings (some days) (manual)
- 1:1 (some days) (manual)
- Write code (mainly LLM assisted))
- Reply to slack messages (manual)
- Review PRs (mainly manual, slightly LLM assisted)
- Continuous learning (manual)
- Generate an end of day summary using LLMs (LLM)
- Daily review (manual)
- Review weekly plan and align (manual)
- Plan next day (manual)