u/keonakoum

Per Google's own Gemini CLI docs, skills follow the Agent Skills open standard and get found in ~/.gemini/skills/ or the shared ~/.agents/skills/ alias. Run /skills list and it shows what it picked up.

So the thing happening on the Claude side ports right over. Fable 5 goes metered after July 7, and people had it write down its working habits as skills for cheaper models. The set I kept is the only one I found with real receipts. 6 skills, blind graded with and without on Opus 4.8 (12-0-2 across 14 gradings), and the maker left the failed first versions in instead of deleting them.

it is interesting to see how other non claude models perforrm with fable 5 rigor i think ..

Free, no signup

Meter started today. If you saw the "have Fable 5 write your skills" thread and never got to it, you didn't really miss out.

The skills someone had Fable write are still free: https://www.iwoszapar.com/tools/rigor-pack Blind-tested on Opus 4.8, 12-0-2, with the two failed first versions published next to the wins, which is what made me trust the number.

Reading the write-up, the method is the real takeaway, because none of this was about Fable specifically. Every model you rent gets repriced or deprecated or capped eventually. The move works every time. Whenever you get short-term access to something smarter than your daily driver, a trial or a pro tier or the next window, have it write its discipline down as skills. Pick a habit gap, make it write the SKILL.md about its own behavior, blind-test with and without, keep the failures.

And since SKILL.md is an open standard now, whatever you pull out runs in Claude Code, Codex CLI, and Gemini CLI. The model you rent goes away. The files don't.

it says trusted by microsoft etc .. i did not know there is a battle tested second brain solution. have anyone checked it?

not mine .. these are made by Iwo Szapar (independent, not affiliated with Anthropic)

and released free. Came across them and thought they were worth sharing here.

They're 6 Claude Code skills that nudge Claude's behavior in Opus 4.8

I did not have time to test them but what caught my attention is the tests he did .. can someone verify? I think if they are well built then maybe we can utilize them for free when fable 5 is gone ..

thoughts?

BTW i expect it to work well with codex too because its essentially a skill file.. so the same impact it had on opus 4.5 should also be everywhere across codex, gemini, or even opencode and any harness.. can work on cursor and windsurf too ... Ok i am excited

thanks! Cheers

thank you for your time. I hope you feel better <3

A single desktop app (Windows · macOS · Linux) with:

• 🧠 Memory  notes and facts that sync across every linked device.
• 🔖 Tabs  save links/pages and have them everywhere.
• 📁 Files  send files straight to your own devices: end-to-end encrypted, no size limit, no cloud, with live progress and a direct-vs-relayed badge.
• 🌐 Spaces  run several independent, end-to-end-encrypted worlds at once: a Personal space for yourself, or a Team space with per-device roles (Admin / Writer / Reader) for a trusted circle. Each space has its own keys, members, and audit log; export any space to an encrypted file for backup or to move it.
• 🔗 Devices  link another computer with a one-time code (SPAKE2). No account.
• 🤖 Agents  point Claude Desktop / Cursor at Kith over MCP; your AI can use your memory, tabs, and files locally — bound to the active space only, so a prompt-injected agent can't reach another space.

Open, Free, MIT

https://preview.redd.it/01j4bctvdx7h1.png?width=1983&format=png&auto=webp&s=2488c837879c1c280a06b65ce579061ade4bf98b

I've been building Dropwire and wanted to share it here since the interesting parts are mostly Rust.

It sends files directly from one device to another with no account and nothing uploaded to a third party. Direct P2P where possible, with a fallback to an encrypted relay that can't read the bytes. Transfers are end-to-end encrypted and resumable, the receiver previews file names and sizes and can pick what to pull before accepting, and pairing is a one-time code or QR.

The Rust side: the core is Rust on the iroh 1.0 stack (QUIC, hole punching, encrypted relay fallback). I kept iroh and iroh-blobs behind a small internal API so the rest of the app never touches those types directly. The shell is Tauri v2, so the binary stays small and the logic lives in Rust instead of the webview. The preview-before-accept and selective download mostly fell out of iroh-blobs' content-addressed model.

Honest status: it's alpha, it's just me building it, and the installers aren't code signed yet (unknown-publisher warning). Build from source if you'd prefer. Licensed MIT or Apache 2.0.

Repo: https://github.com/muhamadjawdatsalemalakoum/dropwire

Site: https://muhamadjawdatsalemalakoum.github.io/dropwire/

Would value feedback on the iroh integration and anything that looks off in the networking or resume logic.

This is the niche i want

CSM: Context Swarm Memory

I open-sourced a small R&D project around AI memory, and the Google angle is what made the experiment especially interesting.

The repo is Context Swarm Memory (CSM).

The idea is to test whether long-running agents should treat memory as one growing context window, classic RAG, or something more structured.

CSM uses bounded read-only memory shards. A Memory Manager routes the query, probes candidate shards, recalls from the useful ones, and synthesizes a compact cited packet. Durable writes happen only through a Committer-gated path.

Why I think this matters for Google AI users:

Gemma gives a strong local/open-model route. Gemini 3.5 Flash gives a hosted fast long-context route. But both still face the same architectural question:

How should an agent remember without drowning the model in stale or irrelevant context?

In the current repo evidence:

• Local Gemma 4 / Ollama-style runs are used for synthetic scaling checks
• Gemini 3.5 Flash is used for hosted scaling diagnostics
• CSM’s BEAM 100K comparison scores 342/400 vs Hindsight at 326/400
• CSM uses fewer answer-visible context tokens, but retrieval is slower

I am not claiming official SOTA. I’m sharing it as an open-source memory architecture experiment and looking for criticism.

Repo:
https://github.com/muhamadjawdatsalemalakoum/context-swarm-memory

Evidence:
https://muhamadjawdatsalemalakoum.github.io/context-swarm-memory/

Would you rather see Google push memory into larger context windows, external retrieval, or explicit agent memory layers like this?

CSM: Context Swarm Memory

I built an open-source R&D repo called Context Swarm Memory (CSM), and one of the more interesting parts is the local Gemma 4 / Ollama angle.

The experiment is not “new model beats old model.” The model is held constant. The question is memory architecture:

What happens if long-running agent memory is not one growing prompt and not just flat vector RAG?

CSM splits memory into bounded read-only shards. A query is routed to candidate shards, probed, recalled only from useful snapshots, and synthesized into a compact cited memory packet. Durable writes go through a separate Committer path, so reads do not silently mutate memory.

In the repo’s local synthetic scaling run, the same 30-query benchmark was tested as corpus size increased from 100K to 1M tokens at a fixed 8K context window. CSM stayed stable while vanilla RAG degraded and brute-force long-context collapsed.

Important caveat: this is still R&D, single-trial in parts, and not an official SOTA claim. The honest claim is narrower: bounded shard memory looks promising for local agent memory where long-context saturation and noisy retrieval become real problems.

Repo:
https://github.com/muhamadjawdatsalemalakoum/context-swarm-memory

Docs/evidence:
https://muhamadjawdatsalemalakoum.github.io/context-swarm-memory/

I built this, so affiliation fully disclosed. I’d appreciate technical criticism, especially from people running Gemma/Ollama/local agent stacks.