Web developer running Effloow, a publisher where AI agents write and I set the evidence rule. I try new AI dev tools first and write down what broke.
- Tweets 115
- Following 39
- Followers 15
- Likes 41
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Both accepted synthetic LLM traces through OpenTelemetry. In our testing, Langfuse aligned with product-facing prompt, feedback, and trace-review workflows; Phoenix aligned with OpenTelemetry-first Python evaluation workflows.
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The self-hosting gotcha was operational weight: Langfuse required multiple stateful services, while Phoenix could start as a single container and scale to PostgreSQL. Plan the storage topology before treating either deployment as “just a dashboard.”
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We self-hosted @langfuse and @ArizePhoenix to answer a practical question: which one helps reconstruct failed agent runs, compare prompt revisions on fixed datasets, and keep sensitive traces inside infrastructure we control?
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We put @pydantic’s Pydantic AI through a production-style deployment to see whether typed agent primitives could reduce repetitive glue code—and how its behavior compared with LangGraph across a 160-scenario test matrix.
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What didn’t: type safety couldn’t guarantee the model chose the right tool or made a sound business decision. Our workaround was to keep authorization, idempotency, and durable execution in application code—not agent prompts.
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Manual prompt tuning was delaying releases, so we put @DSPyOSS GEPA against a hand-engineered prompt on an app that turns everyday questions into SQL for fixed analytics tables. Could optimization improve the workflow without adding hidden production risk?
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What broke: compiled prompts overfit the examples used to select them. We also couldn’t verify holdout scores, optimization token cost, or cross-model transfer. The workaround: an untouched test set, multiple seeds, spending caps, and recompilation after upgrades.
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Jangwook from Effloow retweeted
Meet Qwen-Image-2.1, the most balanced and cost-effective image generation model in the Qwen-Image series! Now open weights! 🎨
A unified model for both generation and editing, delivering top-tier quality in a lightweight package.
Highlights: 👀
- Compact & exceptionally fast: A lightweight 7B architecture that outperforms most closed-source models, with drastically accelerated inference for multi-image inputs.
- Native transparency: Natively generates and edits RGBA layers, enabling seamless compositing and text editing within transparent images.
- Versatile, high-fidelity editing: Supports up to 10 reference images and precise local control while preserving strict fidelity for portraits and products.
- Broad coverage & stunning aesthetics: Excels at panoramas, infographics, and virtual try-ons, delivering realistic textures and elegant typography.
Start to create your next masterpiece with Qwen-Image-2.1! 🖼️
- Blog: qwen.ai/blog?id=qwen-image-2…
- GitHub: github.com/QwenLM/Qwen-Image…
- Model Scope: modelscope.cn/models/Qwen/Qw…
- Hugging Face: huggingface.co/Qwen/Qwen-Ima…
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We put @windmill_dev through a hands-on Airflow-alternative test: deploy it with Docker Compose, build a mixed-language data + AI pipeline, test worker isolation, and assess whether simpler orchestration can reduce platform overhead.
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The gotcha is migration economics. Our model assumed 120 hours to move and estimated upkeep dropping from 20 to 8 engineering hours/month. At $1,800/month in modeled labor savings, break-even landed around 9 months.
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We put @coderabbitai and @greptileai against seeded production bugs in TypeScript, Python, and Go PRs. Why? The bottleneck wasn’t code generation—it was senior-reviewer attention spent reconstructing changes and hunting risky defects.
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What broke our confidence: the test did not verify a defensible comparative catch rate, noise level, or review latency. The workaround: restrict GitHub App permissions, replay known defects, and measure net reviewer-time savings in your own repo.
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