What is the one thing you struggle to cut from your drafts? Is it a specific character, a favorite scene, or perhaps a piece of world-building you’re too attached to let go?
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Most writers skip the hard part: self-editing. They think a good story is enough. It's not.
The new issue of Foundations breaks down why rigorous self-editing is the only real path to a sustainable career.
"A well-edited book builds trust. Trust builds a backlist. A backlist builds a career."
Sign up here: https://jeffreyharlan.com/newsletter
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How do you edit your work?
The editing phase is where the world-building holds together or falls apart. It’s easy to gloss over the mechanics of self-editing, but it’s the most critical part of the craft.
I’m curious about the specific methods people use. Do you have a checklist for continuity? Do you use specific tools for tracking plot threads? Or is it purely intuitive?
Please share your actual process. No fluff, just the work.
How do you edit your work?
In an age of instant publishing, the discipline of self-editing remains crucial. Whether you are crafting a superhero epic or a horror story, the revision process defines the final product.
What is your method? Do you use digital tools, physical copies, or a hybrid approach? How do you handle the continuity challenges inherent in speculative fiction?
Looking forward to hearing from the community.
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Chroma Context-1: Training a Self-Editing Search Agent
https://www.trychroma.com/research/context-1
#HackerNews #ChromaContext1 #SelfEditing #SearchAgent #AIResearch #MachineLearning

Retrieval pipelines typically operate in a single pass, which poses a problem when the information required to answer a question is spread across multiple documents or requires intermediate reasoning to locate. In practice, many real-world queries require multi-hop retrieval, in which the output of one search informs the next. Recent work has shown that frontier LLMs perform this multi-hop search effectively through a process known as agentic search, simply defined as a loop of LLM calls with search tools. This mode of search often comes with significant cost and latency due to their use of frontier-scale LLMs. We introduce Chroma Context-1, a 20B parameter agentic search model derived from gpt-oss-20B that achieves retrieval performance comparable to frontier-scale LLMs at a fraction of the cost and up to 10x faster inference speed. Context-1 is designed to be used as a subagent in conjunction with a frontier reasoning model. Given a query, it produces a ranked list of documents that are relevant to satisfying the query. The model is trained to decompose queries into subqueries, iteratively search a corpus, and selectively edit its own context to free capacity for further exploration.
10 Editing Mistakes First-Time Authors Make (That Could Cost You Readers)
Guest post by Renée Smith. You’ve typed the final word of your manuscript. After months, maybe years of writing, your...
The post 10 Editing Mistakes First-Time Authors Make (That Could Cost You Readers) appeared first on C. S. Lakin.
https://www.livewritethrive.com/2026/02/27/10-editing-mistakes-first-time-authors-make-that-could-cost-you-readers/