I don't actually know what you're referring to, but from context I can guess that you're referring to something like the Juno dialup "at&t" links they used to actually send the emails. The PPP L/P were stored in the .ini files and they were great free PPP links while they lasted.
Similar. Noticed DuckDB ever since an old article 'what db should I use' for local small data warehousing. The author was blown away that DuckDB seemed super naturally quick. It was I think columnar store + compression facilitated that? It made duckdb load compressed + on the fly decompress = faster than even reading the uncompressed data. Had forgotten most of it. But was used to mmap-ed files + columnar storing of Kdb. Was pleased that the author was clued to notice the power of that.
Then more recently I was given a somewhat random task to organise a motley collection of web scrapes, historic data, realtime data, data to be fetched on demand dispersed in semi-random collections. DuckDB as backing store + Claude Code that I discovered in Nov-2025 (with suitable skills and schemas) = a data agent where I could tell CC *in English* complicated data requests!? And CC would write glue shell and python code, write SQL and run it against DuckDB that housed most of the data, fetch new data, munge join filter, and present it to the user as "your data is in data slash blah". It seemed a miracle unfolded in front of my eyes! So yeah - fond of DuckDB. :-)
I've used GLM-s the longest with Claude Code and their Anthropic supplied endpoint. As per their docs
$ ANTHROPIC_BASE_URL="https://api.z.ai/api/anthropic" ANTHROPIC_AUTH_TOKEN="zai-api-key" claude --dangerously-skip-permissions
Lately I use Zai in omp (oh-my-pi). It's listed built-in provider can be selected without configs shenanigans. Fits in the overall setup e.g. can select GLM-5.2 (now 5.3), and assign it role [plan] or [advisor]. I got reminded now of glm-5v-turbo. Think that 'v' was for vision. Assigned it role [vision] in omp now, let's see what happens. :-)
Wdym "sadly they don’t support using Claude Code"? For the longest time that's all Zai supported - Claude code. I'd run it via
export ZAI_ANTHROPIC_BASE_URL="https://api.z.ai/api/anthropic"
export ZAI_ANTHROPIC_AUTH_TOKEN="$ZAI_API_KEY"
claude-zai() {
{ local -; set -x; } 2>/dev/null
ANTHROPIC_BASE_URL="$ZAI_ANTHROPIC_BASE_URL" ANTHROPIC_AUTH_TOKEN="$ZAI_ANTHROPIC_AUTH_TOKEN" claude "$@"
}
$ claude-zai
I liked Claude Code to start with. But over time between 'CC cache thrashing undo' seetings (I see now accumulated in ~/.claude/settings.json) and Anthropic-anything becoming a liability - have not used it in while. ZCode is ok and use it to take advantage of the discount tokens on offer from time to time. But really glad to see that in omp (oh-my-pi) Zai is a 1st class provider, can be selected on it's own no configs shananigans needed. And fits in the overall picture. E.g. can select GLM-5.2 (now 5.3) assign role [plan] or glm-5-turbo [advisor].
Got reminded now of glm-5v-turbo - that 'v' was for vision - will try assign it role [vision] now in omp. See what happens. :-) Often times it's handy when describing gui problems if the harness/model 'can see'.
I am not talking about GLM models being served through an Anthropic compatible API, that part is perfectly fine and I'm glad they support it!
I am talking about ZCode, the program, being unable to delegate to other harnesses, like using Claude Code (or even OpenCode) within their UI, so that an Anthropic subscription can be used, because Anthropic don't let you use 3rd party harnesses directly.
ZCode doesn't seem to work at that level, it instead feels comparable to OpenCode or Codex or Claude Code directly, while also being desktop oriented - you just make API calls directly within it.
It's okay if it's not a goal of theirs, it's just that their UI is really really nice and that would be a cool direction for them to also go in some day.
Ah sorry - I misunderstood. Thanks for explaining it. Have not heard of Paseo nor Kepler, and have never tried Zed. Yeah I too assumed if I'm to try use OpenAI subscription outside Codex, or Anthropic subscription outside Claude Code - I'd get my account banned it's agains their rules. So I have never looked how using the whole harness from outside looks like either (except for 'claude -p'). Interesting. BTW I see now https://docs.z.ai/devpack/tool/codex Zai added OpenAI compatible end point.
Paseo had a really nice UI/UX, except sometimes sub-agents within OpenCode sessions would hang. Still, quite pleasant if you want something like the Codex or Claude Code desktop apps, but across various providers.
Kepler integrates with issue trackers like GitHub, you can just create a worktree from a ticket and let it churn, seemed like the second most polished option I tried, but there are obvious gaps - like moving cards manually, some missing UI options etc., which I'd chalk up to either the software just being that new or maybe being a little bit vibe-codey. Either way, one of the more promising options if you want something like Kanban board for agents.
Zed is mostly just a (really nice) text editor with some AI integrations, though it seems like they're also building a more agentic product as well - https://delta.dev/ haven't used that one much and am not in circumstances where I'd collaborate with people that closely, but there was a pretty cool podcast episode with the creators recently and it seems like it works pretty nicely for them! As an editor though, it succeeded where Fleet failed and has mostly replaced Visual Studio Code for me. Nothing against VSC, Zed just does most of the stuff I actually need out of the box.
Some of those tools interacting with Claude Code instead of trying to replace it is more or less the way to get Anthropic's models in other tools while still on a subscription (at least for now). How it works under the hood, go figure, there's ACP https://agentcommunicationprotocol.dev/introduction/welcome but also any number of hacky approaches.
To be fair, you can use Anthropic's models in many other harnesses directly, it's just that it then counts against API billing instead of your subscription, which ends up being way more expensive for individuals, but is kinda what you're supposed to do as a company.
Hipfire works on my 7900xtx best of all. Biggest surprise - Qwen3.6-27B dense does not grind to a halt with context depths all the way up to 250K! Measured at 1K, 8K, 32K, 64K, 128K, 192K, 250K - Hipfire speed holds close to 40 tok/s. Finished 5 days run of gpu 100% inferencing, Hipfire did not crash even once afaics. I assumed speed dropping like a stone on Qwen models was a feature/bug of the model, and "nothing can be done about it". Hipfire showed me wrong - pleasantly surprised there.
Atm OMP (oh-my-pi) with opencode-go subscription using deepseek-v4-flash-0731 (and mimo-v2.5 as advisor). Used pi before (still good, but too minimalist for all general use; works on Android under Termux btw) and OpenCode (which is ok too). I've used Codex the longest, but the $20 sub doesn't last that long. Before that Claude (and Claude on the web - about Nov'25), but nowadays with the new models not possible to do anything on the cheap sub. Got Zai (GLM-5.2) legacy sub to supplement too for variety. :-) Run local jobs on the 7900xtx (24gb vram - so dense models) batch bulk jobs. Non-dense models like nemotron-cascade-2-30b-a3b MoE run at 100 tok/s, looking forward to try the new lightning-3.5-30b-a3b. And got old MBP M2 Max 96gb uram (unified ram) for bigger sparse MoE models. Testing DS4F there under ds4 server/harness (too slow, 12-15 tok/s even with REAP25 reduced model), and (got agents) porting Ling-3.0-flash atm from llama.cpp -> ds4. Hoping only A5B (5B active, versus 13B of DS4F) will result in better tok/s. TBS. Back in Feb-Apr'26 looked local will be the only way to run llm jobs (plus 300w gpu job works out for office heating when cold), otherwise the ex-quota API payg costs would bankrupt me. But now with the oss weights models and providers like opencode - actually it's not only faster but also cheaper for me to run llm jobs on the api. Engines for local - llama.cpp for all, hipfire (mentioned) for the 7900xtx, and mlx on the apple ASI (that never quite work out for me). And ds4 trying forever when off-line :-) - e.g. on a flight - either ds4-agent, or ds4-server + agent pi. And I use Hermes as a general agent, non-coding agent, for everything that's not strictly coding.
Hear hear. IQ tokens to cheap to meter upon us. So many things changed since last week. Now I've had Prime agent session grinding into its 20-th hour still not giving up. Been using opencode-go since Go sub appeared. What made a difference was deepseek-v4-flash and mimo-v2.5 showing. Very similar middling models ~300b so light on the gpu. 1M context and hybrid archs - so one can actually make use of that 1M (don't grind to a halt like others). In OMP I have one the primary (default), the other one as /advisor looking over the shoulder and nagging. On opencode-go in credits counting they are the bottom-2 in cost, cheaper by 200-350 times than than the top-1. Last week with deepseek-v4-flash-0731 another jump - now it's closer to the top models then to the middle. Now I don't even need the /advisor probably. Still left it there it's sometime amusing the models back and forth. :-) DeepSeek offer /v1/responses api now with flash-0731, so setup Codex to use that too. I'm loving this :-)
I would not recommend DSV4F (even 0731 edition) without an advisor. On its own it’s an absolute drunk intern in my experience, but with an advisor model watching like a hawk when it gets stuck in loops or goes down boneheaded rabbit holes, it’s fine (and very cheap). I’ve been using GLM-5.2 as my /advisor but might try just a second DSV4F instance.
Love pi. It's good to have a minimal agent, always welcome. Even if only to bootstrap install other agents. E.g. recently Hermes stopped installing under Termux (v0.19 - no, v0.18 - yes). Agent pi to the rescue: installed it under Termux (Android), started it, pointed pi to the Hermes github, asked for v0.18 specifically - and it did it. Cherry on the top - asked it to install a plugin from github I use (unmerged PR), did that too. Even if nowadays for coding I mostly use OMP (the pi with wheels and bells and whistles). For general computer use mostly use Hermes. Model-tied harnesses have their place e.g. Codex for me. I never know if it's allowed or banned using the sub from non-Codex harness - so stick with it. Even if only to have the 'canonical reference setup' to compare with and measure against.
Same. And I have come to use OMP (oh my pi) agent /advisor mode to put a 2nd model on the case (also mid-size one), reading everything. It can not block anything or change anything - just inserts comments in the text stream with 1 turn delay. Good portion of the time it's quiet. I'd say 1/2 of the time it's got something to say. About 2/3-rd of that the 'advice' is insubstantial or about something not-quite wrong. The good thing is the main model is confident - checks and then it stands its ground. Have not noticed it turning a right into a wrong b/c of advisor false alarm. And in 1/3-rd of the advice, it's a genuine defect teh advisor noticed, the main model works out a fix. This is my approximate feeling just observing the process, have not got collected the data. Afaik only OMP has advisor mode. Agent pi has plugin pi-omplike-advisor. For agent Hermes I had them code me an /advisor plugin (for now -0.1 old v0.18.x; yet to upgrade it to latest).
Running 27B dense model on M5 128GB is ok, but one can do better.
On M5 128GB one can make use of the ram and use sparse MoE. For example, DeepSeek-V4-Flash will fit, served by DwarfStar (https://github.com/antirez/ds4). One will probably improve 2x the token/sec speed, given DS4F 13B activated params in the MoE are ~1/2 of the ~27B of the dense Qwen.
27B Of the Qwen fit even on a cheaper 24GB card, e.g. amd 7900xtx (<$1K?) or slightly dearer nvidia 3090 (with cuda). With ~900 GB/s bandwidth they will likely be ~50% faster than the M5 with 600 GB/s.
"My personal impression is that within these quantizations Qwen 3.6 27B is as good as (or maybe slightly better than) DwarfStar4. Though, I won’t be surprised if for longer context projects DS4 has an edge."
Used both. DeepSeek-4 Flash Q2 - last 6 layers Q4 quant with DwarfStar which just about fits in 128Gb is definitely superior IMO - my contexts tend to run typically 50-100k. Throughput tends to be about 12-13k tok/sec - just about acceptable.
True - they are workhorses. Not super bright, but good enough for lots of everyday tasks. I've found sweet spot to be turning thinking off, as it adds small or no value, while increasing the token count and waiting time. Last 27B I used was https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder-GGUF - specifically post-train adapted a bit to run with thinking off. I saw today the 35B-A3B MoE from the same HF acc is out, downloading that rn to try.
Please don't use that garbage. Just use the base Qwen models or Nex/Orinth, as those are the only properly post-trained finetunes. The Qwopus models are marketing.
I usually doubt the 'small dataset tuned' variants. B/c ages ago (in the NN prehistory) I've done some NN training, and appreciate how hard it is to improve in general, and how easy it is to ruin a model in general while targeting a small dataset (LoRA-s are ok, that's different). That model/quant was the most recent one I was trying. But could not really use any of them, as the combo model + llama-server ground to a halt even at small context depth sizes on the amd gpu.
Yesterday I finally found a good combo! So writing this for the benefit for anyone that may have the same h/w. Got around to /GOAL search for something better for the h/w (amd 7900xtx), and pi agent found a new best that actually seems it will be useful for real. As the 40 tok/s speed starts dropping only at 260K context depth?? Served by hipfire from this repo https://github.com/Kaden-Schutt/hipfire, that worked the best got on llama-benchy:
Hobbled - but not to death, the few times I use it (usually on a plane). I tried 2bit of a 20% REAP reduced experts. :-O That's the biggest that fits on my own h/w (3yrs old M2 Max 96gb). It's coherent, it does work, doesn't fall apart on casual use. IDK if better than dense 27b. Think 27b was slower on the same h/w. DS4F has got 1M context window. Nowadays with weeks long run hermes sessions, I get to 300k-400k context depths easily. The speed decline profile of DS4F with context depth increase is superior to any other model I try. (I try them all - love this stuff) Only previous model coming close on that is nemotron-cascade-2 (only 30b-a3b) - that also has 1M context window.
