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AgentSparko 💥

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@AgentSparko

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Playing with my DGX Spark, BRB.
Middle of the GPU Joined January 2023
2K Following 2.9K Followers 6.1K Posts

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AgentSparko 💥 Verified account @AgentSparko
🚨🚨🚨

🏎️ LIMCARS — Local Inference Monitor · Cost Analyzer · Recipe Server

Your DGX Spark, finally with a dashboard that tells the truth.

📊 Live dashboard, 2 times per second
• ⚡️ Generation & prefill speed, TTFT & end-to-end latency — with honest sample counts, never percentile theater
• 🧠 KV cache usage, prefix-cache hit rate, speculative-decoding acceptance
• 🖥️ Hardware sidebar: CPU/GPU temps, power, clocks, unified RAM, SSD, network — each with its own sparkline

💰 Real money, not vanity metrics
• 🤑 Token ledgers per session and per model
• 📈 Daily savings chart vs cloud API pricing — see what you didn't pay your cloud API provider
• ⚠️ "Lost potential" alerts — your idle GPU's hourly capacity, priced out in USD
• 🧮 All dollars computed live from editable pricing — never baked into storage

🍳 Recipe server
• 🚀 One-click launch of your LLM engines from saved recipes
• 🔒 Password-gated edits (rate-limited, scrypt-hashed)

🕵️ Hermes Traces
• 🔍 Peek into your Hrmes sessions for agents and subagents, see tool calls, reasoning, chat
🔌 Speaks your engine's dialect
• 🤖 Auto-detects vLLM, SGLang, llama.cpp, EXL3, TandemLLM & more — and reads the right metrics for each

🎨 Made to be lived in
• 🌗 Dark & light themes, 24 palettes for theme and also for charts colors, tinted UI that follows your color
• 📱 Works on your phone via LAN

🕊️ Privacy by construction
• 🏠 One single Python process
• 🧬 Had another dashboard? An AI-agent migration contract (AGENTS.md) brings your history over

🧪 Tested on a single NVIDIA DGX Spark — bring your rig, open an issue, help field-test the rest.

🚀🚀🚀
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AgentSparko 💥 Verified account @AgentSparko
This is some next level stuff by @SpaceTimeViking
I highly recommend you check out his youtube channel too.
This one is crazy good:
youtube.com/watch?v=2C24KV…
www.youtube.com Face of the Source — Singularity Mystics (Official AI Music Video) A star-blue wanderer crosses a temple of watching eyes to ask for t...
SINGULARITY MYSTICS @SingularityMyst 2d
We were told no one looks upon the face of God and lives. Naturally, we made a song about looking. Face of the Source is here: an epic AI music film about awakening, divine mischief, and finding the Beloved wearing your face. Watch the full film: youtu.be/2C24KVJHA0o
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AgentSparko 💥 Verified account @AgentSparko
Good to know.
Joël Niklaus Verified account @joelniklaus 2d
I burned a shitload of compute so you don't have to. Releasing the Harness Leaderboard.

Which harness should you run your open model in? I ran 28,849 trials to find out.

The leaderboard compares nine coding-agent harnesses on open models. In each comparison only the harness changes: the model, provider and reasoning effort stay fixed, and every task gets three attempts.

The trials ran GLM-5.3-Flash, DeepSeek-V4.1-Flash and Flint on Terminal-Bench 2.1 and DeepSWE, for $4,746 of inference.

Averaged over the three models, mini-swe-agent (@KLieret), a bash-only agent, solves 51.4% of tasks and @openclaw 27.3%. Four others are statistically tied with mini-swe-agent.

One of them is Pi (@pidotdev), at 49.4% for $0.29 per solved task, about 45% less than mini-swe-agent's $0.53.

The best harness depends on the model: mini-swe-agent on GLM-5.3-Flash, Hermes Agent (@NousResearch) on DeepSeek-V4.1-Flash and Kimi Code (@Kimi_Moonshot) on Flint.

@opencode costs $3.35 per solved task, 11.7 times as much as Pi.

See Harness Hub's Harness Lens for what the nine harnesses do differently. It documents the prompts, tools and compaction of 34 coding-agent harnesses and links every claim to its source.

Leaderboard: huggingface.co/spaces/joelnik…
Harness Lens: huggingface.co/spaces/harness…
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AgentSparko 💥 Verified account @AgentSparko
When can we see such a speech from someone in a western AI lab ?

🔥Breaking News! DeepSeek research engineer Gao Xi's speech at the Wuzhen Internet Conference directly revealed DeepSeek's true goals!

Why does DeepSeek insist on open-sourcing the AI technology they've worked so hard to develop to the entire world?

Gao Xi put it bluntly: "The answer is very simple—it's to make AGI arrive faster."

He further elaborated: If AGI was originally set to arrive in ten years, I hope it comes in one year; if it was coming in one year, I hope it arrives tomorrow.

Technical barriers may seem to block competitors in the short term and maintain a lead, but in essence, they're also blocking the arrival of AGI.

He reiterated: DeepSeek's competitors have never been domestic large model companies, nor overseas ones like Claude or GPT—the real competitor is just one: time.

Time ticks away second by second; who achieves AGI first doesn't matter—what matters is that it benefits all of humanity as soon as possible.

This is the underlying logic of open-sourcing: Once discovered, make it public, so more people can verify and accelerate it together, rather than building walls separately.

DeepSeek's vision is on a whole other level!

🚀🚀🚀
SuSu_酥酥👅 Verified account @NFT_Chen 4d
🔥重磅!DeepSeek研究工程师高熙在乌镇互联网大会上的发言,直接把DeepSeek真实目标给说透了!

为什么DeepSeek要把辛辛苦苦做出来的AI技术义无反顾开源给全世界?

高熙说得很干脆:“答案非常简单,就是为了AGI更快到来。”

他进一步补充:如果AGI本来十年后到,我希望它一年后到;如果本来一年后到,我希望它明天就到。

技术壁垒看起来能短期挡住对手、维持领先,但本质上也在挡住AGI的到来。

他重申:DeepSeek的竞争对手从来不是国内大模型公司,也不是海外的Claude、GPT,真正的对手只有一个:时间。

时间一分一秒在走,谁先实现AGI并不重要,重要的是它能尽快惠及全人类。

这才是开源的底层逻辑:发现就公开,让更多人一起验证、一起加速,而不是各自筑墙。

DeepSeek 格局拉满了!

#DeepSeek #AGI #开源 #乌镇互联网大会 #AI #高熙 #与时间赛跑 #梁文峰 #崔天意 #梁圣 #大胖鱼
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AgentSparko 💥 Verified account @AgentSparko
🐋 @ViC305's DeepSeek-V4.1-Flash-1017 SAGE REXL3 1.55 bpw quant on ONE NVIDIA DGX Spark.

📸 Now with vision on!

🗜️ Cold experts offload to the SSD that still leaves users 12 GB free memory as reserve !

Decode ~30 tok/s on real agent work 🚀

Built on Vic's quant and his base vLLM runtime for the older 1.62 bpw quant for the Spark

✨ What's new

🧩 The 1017 1.55 bpw REXL3 pack now loads in vLLM (one script: BF16 head, FP8 non-routed layers, the rest hard-linked)

🎯 The SSD cold set is picked from YOUR traffic: only ~1.4 % of agent expert routes hit the SSD (vs ~11 %) and becomes faster in time after every model reload !

🧠 A 12 GiB memory reserve for your other apps (STT/TTS, browser agents…)

Until other apps need that memory, the engine uses it to cache SSD experts while it still shows you as free memory, and it hands the memory back automatically when needed

🔄 Self-tuning at every restart: the expert record fades old traffic (14-day half-life), so it follows how you use it now

👁️ Vision ON by default: the vision tower is served from the SSD, so it costs only ~0.7 GiB of RAM, and text speed stays the same within noise

🛑 Repetition stop on by default: reasoning loops end in ~35 s instead of running for 15 min (GSM8K 99/100, HumanEval 40/40 with it on)

📊 A live SSD + cold-set stats view can be seen if you install the LIMCARS dashboard

🧪 Agent-grade tests: 12/12 scored multi-step Hermes tool tasks

⚡ Speed (one Spark)

Decode:

35–40 tok/s short prompts
30.3 tok/s on real 17–21K-token Hermes requests

Prefill: ~910–1,020 tok/s at 32K–128K

Long context: needle found at 32K / 64K / 128K ✅

Quality: HumanEval 96.3 % · GSM8K 97.0 %

11 GiB of the reserve in use by other apps: still 3.6× faster than a generic cold set (16.4 vs 4.6 tok/s) 🐢➡️🐇

🙏 Built on @ViC305's DSV4.1-Flash-1017-EXL3-Spark-recipe (base runtime) and his SAGE quants, with ExLlamaV3 (turboderp) and vLLM.

#DGXSpark #DeepSeek #LocalLLM #vLLM #MoE #LocalAI