Loop Daily: 2026-03-31
Auto-research is growing up fast. The conversations shifted from "what is it" to "here is what broke on cycle 3." People are forking Karpathy's framework for non-ML use cases, quantifying the economics of tool calls inside loops, and discovering that the boring parts, data pipelines and sleep commands, are where the real engineering lives.
#1
@0xViviennn
https://x.com/0xViviennn/status/2038657899963281725
Forked Karpathy's auto-research to iterate on their own GitHub project Uncommonroute. Ran 6 cycles: Cycle 1 security audit skipped (AI found nothing), Cycle 2 dead code cleanup passed with 100% verification and saved 1.2M tokens, Cycle 3 crashed from over-aggressive changes, Cycle 4 regressed to 96.8% and got rolled back, Cycle 5 hit 100% but failed code review, Cycle 6 still running. They have now pivoted from iterating the project to iterating the framework itself. This is probably the most detailed public auto-research log outside of ML benchmarks.
https://x.com/0xViviennn/status/2038657899963281725
Forked Karpathy's auto-research to iterate on their own GitHub project Uncommonroute. Ran 6 cycles: Cycle 1 security audit skipped (AI found nothing), Cycle 2 dead code cleanup passed with 100% verification and saved 1.2M tokens, Cycle 3 crashed from over-aggressive changes, Cycle 4 regressed to 96.8% and got rolled back, Cycle 5 hit 100% but failed code review, Cycle 6 still running. They have now pivoted from iterating the project to iterating the framework itself. This is probably the most detailed public auto-research log outside of ML benchmarks.
#2
@stefanopopoulos
https://x.com/stefanopopoulos/status/2038678682651545781
Uses this workflow daily: push training jobs to Baseten via MCP, check logs, keep iterating until the target metric works, record experiments in markdown. Multi-node scale auto-research specifically for getting RL libraries working. The fact that someone runs this as a daily routine, not a one-off experiment, is the signal.
https://x.com/stefanopopoulos/status/2038678682651545781
Uses this workflow daily: push training jobs to Baseten via MCP, check logs, keep iterating until the target metric works, record experiments in markdown. Multi-node scale auto-research specifically for getting RL libraries working. The fact that someone runs this as a daily routine, not a one-off experiment, is the signal.
#3
@_itsjustshubh
https://x.com/_itsjustshubh/status/2038695521057984529
Built a Claude Code plugin that uses the agentic loop to run X, LinkedIn, and Reddit autonomously. Computer use in the terminal is the key insight: social media is just another app for the agent to control. Open source. This takes auto-research from code optimization into content operations.
https://x.com/_itsjustshubh/status/2038695521057984529
Built a Claude Code plugin that uses the agentic loop to run X, LinkedIn, and Reddit autonomously. Computer use in the terminal is the key insight: social media is just another app for the agent to control. Open source. This takes auto-research from code optimization into content operations.
#4
@0xyunss
https://x.com/0xyunss/status/2038117908086415664
After building several agents, the biggest realization: dataset is everything. You can have the best LLM and the cleanest agent loop, but without structured data the agent is useless. LLMs cannot read the web natively like humans, but they read markdown and JSON extremely well. Build the data pipeline first, the agent is just a layer on top. This is why startups are racing to pay you for your data.
https://x.com/0xyunss/status/2038117908086415664
After building several agents, the biggest realization: dataset is everything. You can have the best LLM and the cleanest agent loop, but without structured data the agent is useless. LLMs cannot read the web natively like humans, but they read markdown and JSON extremely well. Build the data pipeline first, the agent is just a layer on top. This is why startups are racing to pay you for your data.
#5
@Odalo_Eguabor
https://x.com/Odalo_Eguabor/status/2038674994914320721
Running auto-research on Claude Code to find optimal margins for bids. While everyone else shows off ChatGPT custom builds, this person is using auto-research for real-world commercial optimization. The use case is dead simple and exactly the kind of application that scales.
https://x.com/Odalo_Eguabor/status/2038674994914320721
Running auto-research on Claude Code to find optimal margins for bids. While everyone else shows off ChatGPT custom builds, this person is using auto-research for real-world commercial optimization. The use case is dead simple and exactly the kind of application that scales.
#6
@realWeZZard
https://x.com/realWeZZard/status/2037960110572818634
Discovered a nasty engineering problem: current agentic coding models are not suited for building general agents on instant messaging platforms. Claude Code adds a sleep before long commands, which directly blocks the agent loop. Took significant effort to suppress this behavior. A concrete engineering pain point that anyone building IM-based agents will hit.
https://x.com/realWeZZard/status/2037960110572818634
Discovered a nasty engineering problem: current agentic coding models are not suited for building general agents on instant messaging platforms. Claude Code adds a sleep before long commands, which directly blocks the agent loop. Took significant effort to suppress this behavior. A concrete engineering pain point that anyone building IM-based agents will hit.
#7
@ethereumdegen
https://x.com/ethereumdegen/status/2038662981869826093
Building a horizontally scalable agent loop system where workers claim incoming agent loop requests, become a specialized agent preset for the duration of one job, then return to being generic. Like doppelganger agents. This architecture of ephemeral specialization is a novel pattern for scaling loops.
https://x.com/ethereumdegen/status/2038662981869826093
Building a horizontally scalable agent loop system where workers claim incoming agent loop requests, become a specialized agent preset for the duration of one job, then return to being generic. Like doppelganger agents. This architecture of ephemeral specialization is a novel pattern for scaling loops.
#8
@EMPIRE_ENGINE
https://x.com/EMPIRE_ENGINE/status/2038674221375553623
Nobody benchmarks tool call economics in agentic loops. Every MCP tool call equals latency plus token overhead plus a new failure surface. In a 10-step agent loop with 3 tools per step, that is 30 potential failure points compounding silently. Builders obsess over model choice while ignoring the operational cost of the loop itself.
https://x.com/EMPIRE_ENGINE/status/2038674221375553623
Nobody benchmarks tool call economics in agentic loops. Every MCP tool call equals latency plus token overhead plus a new failure surface. In a 10-step agent loop with 3 tools per step, that is 30 potential failure points compounding silently. Builders obsess over model choice while ignoring the operational cost of the loop itself.
#9
@ghumare64
https://x.com/ghumare64/status/2037862456576319503
Made auto-research scalable with multi-GPUs. A direct extension of the original framework to handle larger-scale experiments. The link goes to their implementation. Moving from single-GPU overnight runs to multi-GPU parallel research is the obvious next step that few have actually shipped.
https://x.com/ghumare64/status/2037862456576319503
Made auto-research scalable with multi-GPUs. A direct extension of the original framework to handle larger-scale experiments. The link goes to their implementation. Moving from single-GPU overnight runs to multi-GPU parallel research is the obvious next step that few have actually shipped.
#10
@thedudesminds
https://x.com/thedudesminds/status/2038657758065529072
Runs OpenClaw 24/7 with cron jobs, browser automation, and a memory system. Says the no-API-key local path is underrated because most people do not realize how much you can do without cloud dependencies. Once the agent loop clicks, the scope of what you can wire up is genuinely surprising.
https://x.com/thedudesminds/status/2038657758065529072
Runs OpenClaw 24/7 with cron jobs, browser automation, and a memory system. Says the no-API-key local path is underrated because most people do not realize how much you can do without cloud dependencies. Once the agent loop clicks, the scope of what you can wire up is genuinely surprising.
#11
@vetolayer
https://x.com/vetolayer/status/2037901880357982689
Building the VetoLayer Risk Calculator. The math: a 100 dollar agentic loop plus 10 minutes of human reaction time equals 60 thousand dollars in treasury drain. Making the invisible risk of unmonitored loops visible. A needed tool as auto-research moves from hobby projects to production environments with real money.
https://x.com/vetolayer/status/2037901880357982689
Building the VetoLayer Risk Calculator. The math: a 100 dollar agentic loop plus 10 minutes of human reaction time equals 60 thousand dollars in treasury drain. Making the invisible risk of unmonitored loops visible. A needed tool as auto-research moves from hobby projects to production environments with real money.
#12
@goodhunt
https://x.com/goodhunt/status/2037747185157108133
Working on an anti-slop auto-research variant, sharing it with Teknium and theemozilla for feedback. The quality control problem in auto-research output is real and addressing it head-on rather than accepting whatever the loop produces is the right instinct.
https://x.com/goodhunt/status/2037747185157108133
Working on an anti-slop auto-research variant, sharing it with Teknium and theemozilla for feedback. The quality control problem in auto-research output is real and addressing it head-on rather than accepting whatever the loop produces is the right instinct.
π‘ Eco Products Radar
Eco Products Radar
Claude Code (5 mentions), OpenClaw (3), MCP (4 mentions as infrastructure), Baseten (1), Karpathy's auto-research framework (referenced 4+ times as base for forks).
Claude Code (5 mentions), OpenClaw (3), MCP (4 mentions as infrastructure), Baseten (1), Karpathy's auto-research framework (referenced 4+ times as base for forks).
Comments