aufklarer (Ivan)

replied to their post 19 days ago

I hear you — working code and not burning through your quota matter way more than any internal architecture diagram.

I’m looking at the architecture mainly to understand why some tools feel more reliable and easier to debug than others.

What’s been your experience so far — any setup that actually feels close to “it just works”?

reacted to their post with 🔥 21 days ago

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577

I did deep dive comparison of Claude Code vs OpenAI Codex code agents architectures, interesting what is your personal experience on this?

Both Claude Code and OpenAI Codex are built on the same backbone: a single-agent event loop that repeatedly thinks, calls tools, inspects the result, and repeats until it’s done. No swarms, no hidden graph orchestration — just one reflective agent iterating through a ReAct-style cycle.

https://blog.ivan.digital/claude-code-vs-openai-codex-agentic-planner-vs-shell-first-surgeon-d6ce988526e8

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reacted to their post with 🔥 21 days ago

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1448

Couple months ago I fine‑tuned Qwen3 Embeddings with LoRA on the LSPC dataset. This time I went the opposite way: a small, task‑specific 80M encoder with bidirectional attention, trained end‑to‑end. It outperforms the Qwen3 LoRA baseline on the same data (0.9315 macro‑F1 vs 0.8360). Details and code: https://blog.ivan.digital/beating-qwen3-lora-with-a-tiny-pytorch-encoder-on-the-large-scale-product-corpus-afe536de205f

posted an update 22 days ago

Post

577

I did deep dive comparison of Claude Code vs OpenAI Codex code agents architectures, interesting what is your personal experience on this?

Both Claude Code and OpenAI Codex are built on the same backbone: a single-agent event loop that repeatedly thinks, calls tools, inspects the result, and repeats until it’s done. No swarms, no hidden graph orchestration — just one reflective agent iterating through a ReAct-style cycle.

https://blog.ivan.digital/claude-code-vs-openai-codex-agentic-planner-vs-shell-first-surgeon-d6ce988526e8

3 replies

·

posted an update about 1 month ago

Post

1448

Couple months ago I fine‑tuned Qwen3 Embeddings with LoRA on the LSPC dataset. This time I went the opposite way: a small, task‑specific 80M encoder with bidirectional attention, trained end‑to‑end. It outperforms the Qwen3 LoRA baseline on the same data (0.9315 macro‑F1 vs 0.8360). Details and code: https://blog.ivan.digital/beating-qwen3-lora-with-a-tiny-pytorch-encoder-on-the-large-scale-product-corpus-afe536de205f

reacted to their post with 🔥 about 1 month ago

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3265

Fine-Tuning Qwen3 Embeddings for product category classification on the Large-Scale Product Corpus

Language-models such as GPT, Llama, DeepSeek, Qwen trained with a filtered slice of Common Crawl. For e-commerce work, though, we can start with the Web Data Commons (WDC), the project by the University of Mannheim. It extracts web pages that carry some metadata and publishes the result as the Large-Scale Product Corpus (LSPC).

Search engines like Google reward pages that include detailed product markup, so merchants already populate their sites with SEO-friendly fields such as title, brand, GTIN, price — and, crucially, category labels. Thanks to these built-in annotations, the WDC Large-Scale Product Corpus arrives almost fully self-labelled. I used those labels to fine-tune Qwen3 Embedding with Low-Rank Adaptation (LoRA), code is available on github. The resulting 615 million-parameter checkpoint fits comfortably in limited GPU memory yet updates the model’s representation space, mapping raw product titles to six top-level categories with a macro-F1 of 0.836 (83.6 %).

More details: https://blog.ivan.digital/fine-tuning-qwen3-embeddings-for-product-category-classification-on-the-large-scale-product-corpus-3a0919506bc8

posted an update about 1 month ago

Post

3265

Fine-Tuning Qwen3 Embeddings for product category classification on the Large-Scale Product Corpus

Language-models such as GPT, Llama, DeepSeek, Qwen trained with a filtered slice of Common Crawl. For e-commerce work, though, we can start with the Web Data Commons (WDC), the project by the University of Mannheim. It extracts web pages that carry some metadata and publishes the result as the Large-Scale Product Corpus (LSPC).

Search engines like Google reward pages that include detailed product markup, so merchants already populate their sites with SEO-friendly fields such as title, brand, GTIN, price — and, crucially, category labels. Thanks to these built-in annotations, the WDC Large-Scale Product Corpus arrives almost fully self-labelled. I used those labels to fine-tune Qwen3 Embedding with Low-Rank Adaptation (LoRA), code is available on github. The resulting 615 million-parameter checkpoint fits comfortably in limited GPU memory yet updates the model’s representation space, mapping raw product titles to six top-level categories with a macro-F1 of 0.836 (83.6 %).

More details: https://blog.ivan.digital/fine-tuning-qwen3-embeddings-for-product-category-classification-on-the-large-scale-product-corpus-3a0919506bc8

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