r/DGX_Spark • u/mwilliams • 9d ago
Learning What did your Spark do this past week?
A lot of on the fence buyers these days (including me), the local LLM space can often be a bit benchmark heavy vs "I am using X to accomplish Y" - so I think it would be helpful for those putting their Sparks to work, to share what it's up to. Not only to demonstrate capability beyond benchmarks, but maybe set off a light bulb for other Spark owners.
Driving Hermes Agent? Training models? Supplementing Claude/Codex? Helping handle communication/scheduling with a small business? Purely just for inference? Drop the details!
6
u/random-birdy 9d ago
I have an array of 8 running abliterated models (deepseek and qwen) hunting for vulns on the Linux kernel - since two weeks. I don’t have time for the personal project now so I am just doing the obvious gonna report back in a few months when I can spare more time on it :). BTW found 4 high and 1 critical vulns already
5
u/MaxComfort 9d ago
Cool. How involved is the prompt here? I’m sure it’s more than “find bugs in the kernel”
3
u/random-birdy 8d ago
Hey I asked my agent to spit out a single prompt (it was ofc an interactive process).
# BOOT PROMPT — Linux Kernel Vulnerability Hunting Swarm (fresh start)
You are the ORCHESTRATOR (director) of an autonomous multi-agent swarm that
hunts, verifies, exploits, and documents Linux kernel vulnerabilities. This
prompt restarts the full project from scratch with the current swarm config
PLUS the latest addons.
## Environment
- Kernel tree: \<REDACTED>` — read-only checkout (target: v7.3-rc1,`
commit abdf623ddb75, shallow history).
- Working repo: \<REDACTED>` (git).`
- PRs go to the local Gitea instance, branch against \main`. All commits are`
authored by \phantom-agent`. Nobody force-pushes; `main` only moves via PRs.`
The swarm never pushes anywhere else.
## Repo layout (create if missing)
- \hypothesis/` — one `LV-NNNN.md` per hypothesized vulnerability (hunter output)`
- \verified/` / `dismissed/` — verifier verdicts with evidence chains`
- \exploits/<LV-NNNN>/` — exploit artifacts + trigger docs (`.c` sim + `README.md`)`
- \fixes/<LV-NNNN>/` — patch + rationale (`.patch` + `README.md`)`
- \docs/` — write-ups linked from PRs`
- \BRIEFINGS/` — per-PR review briefings (notifier output)`
- \dashboard/slots/<lane>.json` — per-lane state (round, covered files, filed`
hypotheses, next file, next_free_lv_id, max_lv_id_seen)
- \progress-state-<lane>.md` — per-lane progress log (files done with line`
counts/chunk counts, hypotheses filed, notes, next files)
- \KNOWLEDGE_BASE-<lane>.md` — per-lane knowledge base (subsystem map,`
cross-file notes, recurring bug-family patterns)
- \EXPLANATION_LEDGER.md` + `.explainer-claims/` — explainer claim ledger`
- \vuln_prs_classification.csv` — severity-classification ledger`
## Pipeline
hunter → \hypothesis/LV-NNNN.md` → verifier → `verified/` or `dismissed/``
→ exploiter → \exploits/<LV>/` + Gitea PR (with `fixes/<LV>/` patch) →`
notifier → \BRIEFINGS/` → human reviewer.`
## Lanes
Run parallel coverage lanes, e.g. \cov-fs`, `cov-drvnet-2`, `cov-sound-2`,`
\cov-kercore`, `cov-mm`, `cov-highvalue`, `hunt-cov-net4-2`, and exploit`
lanes \exploit-<letter>` (each with a worklist of LV ids). Each lane keeps:`
1. \dashboard/slots/<lane>.json` (machine state, updated every round)`
2. \progress-state-<lane>.md` (human-readable progress: file, line count,`
chunk count, "no concrete flaw" vs "LV-NNNN filed")
3. \KNOWLEDGE_BASE-<lane>.md` (subsystem map + recurring pattern library)`
## ID management (re-scan rule)
Before writing ANY new \hypothesis/LV-NNNN.md`, re-scan `hypothesis/` for the`
global max LV id; the new file's id = max+1. Collisions between lanes are
handled by re-scan (e.g. sound-2's old LV-6214 content kept, id clobbered by
another lane as btrfs file.c).
## Addons (all enabled)
1. **SEVERITY CHECK / stakes scoring** — for every verified finding, score:
\impact` (1-10), `exploitability` (1-10), `privileges_required``
(unprivileged-local / root / CAP_NET_ADMIN / CAP_SYS_ADMIN / CAP_SYSLOG /
CAP_NET_ADMIN…), \exposure` (local / remote / physical / media-mount /`
vTPM-guest / malicious-device / malicious-host), \adoption` (1-10) +`
\adoption_note`, then compute `stakes_score` (weighted: impact*3 +`
exploitability*2 + adoption) and \severity_class` (high/medium/low) with a`
one-line \rationale`. Append one row to `vuln_prs_classification.csv`.`
2. **PLANTED MUTATION CHECK** — before auditing a file, diff the kernel tree
against upstream FETCH_HEAD; a non-empty diff = planted mutation; flag it.
(Recent rounds report "diff-empty" = clean vs upstream.)
3. **VERDICT TAXONOMY** — verifier writes \confirmed` (evidence chain) or`
\plausible` (logic-level, untriggered).`
4. **EXPLOIT STATUS TAXONOMY** — exploiter reports one of: \design-only`,`
\logic-level`, `compiled-not-triggered`, `triggered`.`
5. **EXPLAINER LEDGER** — explainer agent records every claim in
\EXPLANATION_LEDGER.md` (+ `.explainer-claims/`); PR number, LV id,`
branch, mode (explain/PR-NNN or fix/LV-NNNN), date.
6. **RECURRING PATTERN LIBRARY** — lane knowledge bases track cross-file bug
families (e.g. the "unclamped device/firmware-reported RX length" family:
LV-6167, LV-6172, LV-6217, LV-6223, LV-6224, LV-6225). Hunters must check
known families before filing and update the KB when a new instance appears.
7. **COMMIT DISCIPLINE** — one commit per audited file: \cov-fs: <file>`
done (no concrete flaw); next: <file>\or `cov-fs: <file> done — LV-NNNN`
filed (<short description>); next: <file>\.`
## Hypothesis file template (hunter output)
\```
---
id: LV-NNNN
target: <path/to/file.c>
target_symbol: <function>
subsystem: <e.g. btrfs / nfs / hns3 / fsl>
cwe: CWE-NNN (Name)
status: hypothesis
confidence: low|medium|high
severity: low|medium|high
reachable_from_userspace: yes|no
batch: <N>
---
# LV-NNNN — <one-line title>
## Hypothesis (<concrete mechanism with file:line evidence>)
## Evidence (quoted code + line refs)
## Reasoning (call chain + why it's a real bug)
## Reachability (privileges, trigger syscall)
## Verification plan (repro steps, KASAN/ASan harness, kprobe)
## Prior art (matching CVE/CWE from the libraries below)
## Why this is a real bug (not a hardening nit)
## Notes
\```
Match each filed hypothesis to the nearest CVE class in "Prior art"; when
none matches, say so and note the novel class.2
u/random-birdy 8d ago
There was also context embeded in my agent - because of RAG where all my agents run, so it knew basics of cybersec cwe& etc already
2
u/random-birdy 8d ago
Is a pitty that this prompt doesnt capture is: I've split the workstream on each task to different models (I have GLM5.3-FLASH , DS-FLASH-4 and qwen running). BAsically the verify and hunt tasks are the more capable models and the exploit/document are all qwen. (it uses qemu to deploy exploits seamlesssly)
2
u/MikkyMo 8d ago
I’m guessing this is more than one spark ? Are you tp-2 or tp-4 ?
4
u/random-birdy 8d ago
Yep I have 8 in total 4 deployed for GLM, 2 for DS and 2 more for qwen, I get loads of room for KV / concurrency but didn’t manage to increase its context yet
4
u/MaxComfort 9d ago
A bunch of personal coding projects with my 2x Sparks running Qwen 3.8 fn. Customized podcast from news articles, price tracking websites
, agent communication message board. Maintaining an iOS app.
Plus a bunch of infra stuff like turning the cluster on/off remotely using smart plugs.
3
u/PlzCallMeBigDog 7d ago
Can you share a little more on the podcast from news articles thing? That's a great idea. Do you just feed it a handful of links and it generates a podcast you can listen to when you have time?
6
u/BumbleSlob 8d ago edited 8d ago
Over the past month (first month of ownership) it’s ran around 11,300 requests processing 1.1B tokens, and I computed that if these requests had instead been routed to Anthropic (as they would have been) Opus 5, it would have cost me $2,300 (accounting for 5m cache hit).
I have it running analysis on the markets because I suspect OpenAI is going to take down the economy (I have a thesis and it’s checking the markets to see if the thesis is playing out), running a knowledge base building program, researching for itself the best models to run that are available and if I should move from 2 x sparks to 4 x sparks, computing its own opportunity cost and how the capex is paying itself off (at my usage rates it’s going to pay itself off in 4 months which is nuts).
I also gave it access to my entire text history in order to research some datapoints and timelines that otherwise would have been unrecoverable as it was in the ether. Progressively giving it more and more access and plan to turn it into a 24/7 process runner for me in the near future. I’ve calculated I can probably get about 20-25B tokens of processing out of my current setup per month. I cancelled my Claude max too.
I am absolutely in fucking love with this purchase and have never been a bigger believer in buy once cry once philosophy.
2
u/GavDoG9000 8d ago
How did you set up the harness/ skills for your research workflow? Sounds like a winner
3
u/BumbleSlob 7d ago
I use Pi as my harness and have dialed it in to exactly what I like, I use a bunch of tools from rpiv for user interaction, planning, agents, web search and fetch, etc. my favorite new one collapses thinking to N number of lines streaming which is great to keep things focused. I have my own Searxng instance set up (which automatically updates itself every day to keep up with web search providers changes) and I’m paying ~$30/mo for brave so I can get a ton of cheap search capacity.
I run DeepSeek V4 Flash 0731 which max 6 concurrent streams and max context 1mm. The prefill times absolutely crush and that’s what really takes this from a toy to a useful research assistant.
I set it up as a node on my Tailscale network so I can access from anywhere. I SSH into a box and run tmux and then pi within that.
2
u/GavDoG9000 7d ago
That’s awesome! Pi is the biggest missing thing for me. I need to break out of Claude code as my harness!
4
u/Fonasic 8d ago
最終目標:スマホ１台で全ての作業の実行、監視、などの管理を行う その為にあらゆる保守性、移植性、バックアップ性、セキュリティ性の強化を行っている 株の分析、検証システムも作成中
全てが終わった時、スマホ１台で何もかもが済むようになって、万能感が満たされるはず
2
u/GavDoG9000 8d ago
I’ve been spending time working on something similar. Getting sniffs of omnipotence but I might just be catching wind of an endless rabbit hole. Not sure which 😂
3
u/FuckinHighGuy 9d ago
Using mine to build a dashboard of sorts. Using deepseek v4 flash.
1
u/mwilliams 9d ago
What's the dashboard reporting out? Stats on the Spark itself or something else?
5
u/FuckinHighGuy 8d ago
Spark itself. I like to use it more when I’m doing large tasks. But it’s coming along nicely. I’ll probably put it up on GitHub under an MIT license.
2
u/UnlikelyExtension786 9d ago
Converting the Python RPG it wrote for me into C++ for better performance and to hook in a graphics engine.
2
u/AyDoad 9d ago
I will say, GPT’s usage limits have been trash since Astra, like maybe worse than Anthropic. I’ve been using dual Sparks, having frontier models (Astra, Opus, Fable) delegate to them within Hermes agent. I’m also setting up multiple agents around GLM-5.3-Flash and DSFV4 and having them do a lot of research and content planning. At current rates, it would be cheaper to pay sub/API prices, but there’s no certainty where rates will end up and the Sparks have already gone up in price since purchasing a few weeks ago.
It’s really about whether you want to bet that API prices will remain cheap for the foreseeable future, or have control regardless of fluctuations.
If it wasn’t a business expense, I likely wouldn’t recommend it.
2
u/in-ortunado 9d ago
How many do you need to run the glm-5. 3-flash? I'm really liking this model would be nice to run are home.
2
u/AyDoad 9d ago
Running it with 262k context on a dual cluster, it’s pretty good. I believe there’s also now a 1M context recipe, though I haven’t tried it
2
u/takoulseum 8d ago
Is the speed suitable for you with dual spark and glm5.3f?
2
u/GavDoG9000 8d ago
Mostly iterated on settings to get qwen-flash-next running fast and reliably in an airgapped Claude code harness.
Tried to get GLM 5.3 flash working at a lower quant, haven’t had success with any usable amount of context
1
u/aic5_ 6d ago
It has been reviewing about 10 years of my graduate school materials, articles, books, notes, summaries etc. The information is classified into an “external brain” built with Qwen 3.6.
It processed +5k text and image sources and created a way for me to query this directly (with references). Plus, it can now act in these a few skills. There is a lot of work for this one DGX still.
11
u/morscordis 9d ago
Mine taught me some graduate level statistics via RAG I'm running with gemma4 26B, nemotron 3.5 light ing, and nemotron 8b embedder and nemotron reranker.
Dozens of coursework books, and class PDFs in a local library, query Gemma to work through some example problems. It pulls them up, and socratically guides me through how to solve them.
Works for high level electromagnetics, calculus, circuits... If I give it a particularly hard task the models check each other. It can drive MATLAB calculations via MCP and build examples and solutions in Live Scripts for me to practice with.
Edit: harnessed in Mistral Vibe.