Source: github | Overall 7.9/10 | Corroboration: 1
Signal 10.0
Novelty 5.1
Impact 8.3
Confidence 7.0
Actionability 6.5
Summary: Straight from my .agents directory.
- What happened: Straight from my .agents directory.
- Why it matters: Straight from my .agents directory.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
Straight from my .agents directory.
What's new
Approaches like GSD, BMAD, and Spec-Kit try to help by owning the process.
Key details
- My agent skills that I use every day to do real engineering - not vibe coding.
- Developing real applications is hard.
- Approaches like GSD, BMAD, and Spec-Kit try to help by owning the process.
- But while doing so, they take away your control and make bugs in the process hard to resolve.
Results & evidence
- If you want to keep up with changes to these skills, and any new ones I create, you can join ~60,000 other devs on my newsletter: Two ways in, two philosophies.
Limitations / unknowns
- Generalization outside curated tasks is still unclear.
Next-step validation checks
- Reproduce one claim with a public baseline and fixed evaluation settings.
- Check robustness on out-of-distribution or long-context cases.
- Track whether independent teams report matching results.
Source: github | Overall 7.8/10 | Corroboration: 1
Signal 10.0
Novelty 5.1
Impact 8.2
Confidence 7.0
Actionability 6.5
Summary: An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.
- What happened: An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.
- Why it matters: An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
For file submission/navigation questions, see Navigation and file context.
What's new
Windows users can jump to the PowerShell-first Windows install and release quickstart.
Key details
- github.com/code-yeongyu/lazycodex github.com/Yeachan-Heo/gajae-code Join the Discords: ultraworkers discord · gajae-code discord Important Claw Code is not the serious production project here.
- This repository is closer to a museum exhibit than a product pitch, a crustacean-run artifact kept alive by clawed gajaes, swept and labeled by agents, and automatically maintained according to the harnesses above.
- As already described in the project philosophy, this is not meant to be hand-operated like a normal product repo.
- It is an agent-managed exhibit: the harnesses plan, execute, verify, label, and preserve the artifact while the crabs keep the tank running.
Results & evidence
- No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.
Limitations / unknowns
- Generalization outside curated tasks is still unclear.
Next-step validation checks
- Reproduce one claim with a public baseline and fixed evaluation settings.
- Check robustness on out-of-distribution or long-context cases.
- Track whether independent teams report matching results.
Source: hackernews | Overall 6.2/10 | Corroboration: 1
Signal 9.0
Novelty 4.0
Impact 5.3
Confidence 6.2
Actionability 3.5
Summary: When I awoke on RTO Day (officially “Remain to Office,” since management maintained that I’d misremembered and we’d always been in office six days a week), I was not as excited as.
- What happened: When I awoke on RTO Day (officially “Remain to Office,” since management maintained that I’d misremembered and we’d always been in office six days a week), I was not as.
- Why it matters: When I awoke on RTO Day (officially “Remain to Office,” since management maintained that I’d misremembered and we’d always been in office six days a week), I was not as.
- What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
When I awoke on RTO Day (officially “Remain to Office,” since management maintained that I’d misremembered and we’d always been in office six days a week), I was not as excited as I should have been.
What's new
When I awoke on RTO Day (officially “Remain to Office,” since management maintained that I’d misremembered and we’d always been in office six days a week), I was not as excited as I should have been.
Key details
- I felt queasy at the thought of signing the Memorandum of Loving RTO as a condition of my continued employment.
- I was an Associate Slop Doula for Mondo Mayo, founded as a mayonnaise company, which now sells consumer packaged goods, software as a service, and surveillance technology, and is one of three large corporations remaining on Earth.
- At the time, I didn’t think a full, six-day-per-week, fourteen-hour-per-day, in-office schedule was necessary to discharge my duties clicking the GENERATE button, followed by the APPROVE button, thousands of times a day on SlurryHose.
- But experience has shown me that I was wrong.
Results & evidence
- No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.
Limitations / unknowns
- Generalization outside curated tasks is still unclear.
Next-step validation checks
- Reproduce one claim with a public baseline and fixed evaluation settings.
- Check robustness on out-of-distribution or long-context cases.
- Track whether independent teams report matching results.
Source: rss | Overall 4.6/10 | Corroboration: 1
Signal 7.3
Novelty 4.0
Impact 2.0
Confidence 3.0
Actionability 3.5
Summary: Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic
- What happened: Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic
- Why it matters: Could materially affect near-term AI workflows.
- What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic
What's new
Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic
Key details
- Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic
Results & evidence
- No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.
Limitations / unknowns
- Generalization outside curated tasks is still unclear.
Next-step validation checks
- Reproduce one claim with a public baseline and fixed evaluation settings.
- Check robustness on out-of-distribution or long-context cases.
- Track whether independent teams report matching results.
Source: rss | Overall 4.5/10 | Corroboration: 1
Signal 7.3
Novelty 5.1
Impact 2.0
Confidence 3.0
Actionability 3.5
Summary: OpenAI is expanding support for journalism with tools, training, and partnerships for students, educators, journalists, and news organizations.
- What happened: OpenAI is expanding support for journalism with tools, training, and partnerships for students, educators, journalists, and news organizations.
- Why it matters: OpenAI is expanding support for journalism with tools, training, and partnerships for students, educators, journalists, and news organizations.
- What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
OpenAI is expanding support for journalism with tools, training, and partnerships for students, educators, journalists, and news organizations.
What's new
OpenAI is expanding support for journalism with tools, training, and partnerships for students, educators, journalists, and news organizations.
Key details
- OpenAI is expanding support for journalism with tools, training, and partnerships for students, educators, journalists, and news organizations.
Results & evidence
- No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.
Limitations / unknowns
- Generalization outside curated tasks is still unclear.
Next-step validation checks
- Reproduce one claim with a public baseline and fixed evaluation settings.
- Check robustness on out-of-distribution or long-context cases.
- Track whether independent teams report matching results.
Source: rss | Overall 4.1/10 | Corroboration: 1
Signal 7.3
Novelty 5.1
Impact 2.0
Confidence 3.8
Actionability 3.5
Summary: BenchMIRT: What are LLM benchmarks actually measuring?
- What happened: BenchMIRT: What are LLM benchmarks actually measuring?
- Why it matters: Could materially affect near-term AI workflows.
- What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
BenchMIRT: What are LLM benchmarks actually measuring?
What's new
BenchMIRT: What are LLM benchmarks actually measuring?
Key details
- BenchMIRT: What are LLM benchmarks actually measuring?
Results & evidence
- No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.
Limitations / unknowns
- Generalization outside curated tasks is still unclear.
Next-step validation checks
- Reproduce one claim with a public baseline and fixed evaluation settings.
- Check robustness on out-of-distribution or long-context cases.
- Track whether independent teams report matching results.