For papers/articles processing I use AI with pre-defined instructions to create short/medium summaries. The summary should give me good understanding of the main idea and if its worth reading it deeper. Even if not - main idea would be in your head.

These summaries I accumulate into hierarchical knowledge base, optimized for Obsidian and llms https://github.com/sermakarevich/ai_knowledge_wiki

I use it in quite a few repoes:

-- https://github.com/sermakarevich/ai_knowledge_wiki Curated extraction of summaries from AI-related research papers, organized as a hierarchical wiki optimized for Obsidian and LLMs

-- https://github.com/sermakarevich/chunker/tree/master/output/... Chunker processes a document into a hierarchy of self-sufficient chunks and multi-level summaries, producing a set of linked markdown files that an AI model (or a human) can explore through progressive disclosure -- starting from a high-level overview and drilling into details on demand, without ever loading the entire document.

-- https://github.com/sermakarevich/kaggle_wiki A structured knowledge base of Kaggle competitions — solutions, notebooks, and indexes — built for fast lookup and reuse.

After few presentations of sddw to different companies, most important conclusion was that the ssd plugin should be customizable. It should fit the typical size of tasks/features you are working on, specs should fit your requirements, set of steps can be different.

So I created claude code workflow (ccw) which allows to compile custom version of workflow on top of sdd approach: https://github.com/sermakarevich/ccw

After making few presentations of sddw to different companies,

-- https://github.com/sermakarevich/chunker/blob/master/.sddw/c...

-- https://github.com/sermakarevich/chunker/blob/master/.sddw/m...

- ccw (claude code workflow - plugin to compile generic claude code workflows based on sdd approach) - https://github.com/sermakarevich/ccw/blob/main/.sddw/claude_...

Btw you can use ccw to create your own custom version of sddw quite fast - with specs format and sequence of steps that suit you best.

-- writing documents for each other takes weeks - write the document, give people time to review the document, discuss, update on review.

-- writing documents for Claude takes minutes rather than hour - AI is used to assist in writing specs, personal instead of collective review/approval process.

Also beneficiaries are different:

-- when you write spec for Claude - you benefit personally, you have higher chance of getting code you need from ai coder

-- when you write spec for the company - the company is the main beneficiary, it get higher chances to get teams on the same page with the change

At the end of 2025 I switched to Claude Code. Compared to Cursor this opened a different level of automation, including fe possibility of running swarms of agents: https://news.ycombinator.com/item?id=48407998 using subscription limits.

So I spend all my time rather understanding how to squeeze everything possible from AI than myself. AI scales, I am not.

Hierarchically organized Wiki of ai papers / articles for Obsidian / LLM. Approximately 300+ papers in total. You can get most recent +- based on commits. I process ~100 papers/articles a month, so pull from time to time.

Discussion on hn: https://news.ycombinator.com/item?id=48231575

Repo: https://github.com/sermakarevich/sddw

Slides: https://docs.google.com/presentation/d/1SjKXF7hkoqyiN9-3tBGY...

- CLAUDE.md is a terrible abstraction. These files load unconditionally, they often contain descriptions irrelevant to the task at hand, and they stack from your working directory upward. The result is wasted tokens and confusion from injecting irrelevant instructions into the session.

- Skills are bad, but not as bad as CLAUDE.md. They use a progressive disclosure approach: only the skill description goes into the session, and Claude loads the full skill text with a tool when it's needed. That's one level better, but it still doesn't let you scale — you can't create 10K skills, as that would eat your entire usable context. Claude recently introduced a skills budget that silently drops less frequently used skills from the session entirely. You can still invoke them in an interactive session, but the model can't invoke them in a background session.

- Some plugins may be installed more than once. During cleanup I found that a few of mine were installed in multiple locations, consuming double the tokens on duplicated instructions.

- Attaching plugins to every session is a bad idea at scale. You want to be precise about which plugins are actually useful and attach them per task.

- Use a hierarchical knowledge base instead of CLAUDE.md / skills / plugins. It lets you benefit from real progressive disclosure: keep your instructions and tool descriptions in it and let Claude navigate through it quickly and cheaply.

- System tools consume ~15K tokens (7% of the session). You can't manage this — they're just attached, and disabling tools doesn't remove them from the context.

- AskUserQuestion isn't available in background sessions. You need to implement your own tool — MCP- or CLI-based — to give `claude -p` the ability to talk to you.

- You become selective about which model handles each task. Decompose work into harder and simpler subtasks so you can route the simpler ones to weaker, cheaper models and save tokens.

- Your context-switching skill improves over time.

Fleet repo: https://github.com/sermakarevich/fleet

Comments URL: https://news.ycombinator.com/item?id=48407998

Points: 10

# Comments: 6

and not only for coding but also for non coding tasks - like fe deep marketing/sales research https://news.ycombinator.com/item?id=48306730

Here is the solution to it. Built on a SQLite DB and MCP, blocking until the question is answered, supporting all possible question types, with a CLI or web interface for answers, `ask_human_question` fills the gap in efficient subagent management.

https://news.ycombinator.com/item?id=48320233

Built on a SQLite DB and MCP, blocking until the question is answered, supporting all possible question types, with a CLI or web interface for answers, `ask_human_question` fills the gap in efficient subagent management.

Repo: https://github.com/sermakarevich/claude/tree/main/mcp/ask_hu...

Comments URL: https://news.ycombinator.com/item?id=48320233

Points: 1

# Comments: 0

have you seen this happening after auto was introduced?