a11

377 posts

a11 banner
a11

a11

@StreamVC_

run + serve LLMs on hardware you already own. benchmarks, configs, teardowns.

Katılım Eylül 2025
286 Takip Edilen197 Takipçiler
a11
a11@StreamVC_·
within a year there's most likely an open model that matches fable 5 for a fraction of the price. i'd bet on it. once that lands, paying frontier rates for everyday work stops making sense, and inference on open models goes vertical. the frontier still keeps the hardest tasks - everything under that moves to whatever's open and cheap.
English
0
0
3
79
a11
a11@StreamVC_·
the loop table @_proxystudio quoting is output from the operator tooling i've been building for wstDIEM. live loop sizing, liquidation monitoring, exit rehearsal, fail-closed by default. what's next is the question that matters for bigger capital: how much the pool can absorb base:0xf4d97f2da56e8c3098f3a8d538db630a2606a024 today before slippage + the borrow cap eat the edge. plus a demand tracker - this yield is downstream of inference flow, so you should see it turning before the apy does. building the intelligence layer for this strategy.
liquid 💧@_proxystudio

Yes - the correct view here is that this is extremely exciting and we are directing our attention on figuring out how to attract liquidity to the vault & open up this leveraged yield farming strategy to much larger pools of capital

English
0
0
5
167
a11
a11@StreamVC_·
appreciate it. the net-apy grid is the easy half - for bigger capital the real question is how much the pool can actually absorb before slippage + the morpho borrow cap eat the edge. that capacity readout is what i'm building next, plus a demand tracker since the yield's downstream of inference flow. want allocators to be able to size this safely, not just stare at a nice number.
English
1
0
1
34
a11
a11@StreamVC_·
ran the wstDIEM loop numbers for anyone eyeing it. what stands out: at current pool utilization morpho borrow is ~0.5%, basically free. so once there's depth to loop into, the returns hold up even at today's realized yield - ~10% at 2x, 15% at 3.3x, 19% at 4.4x. if the inference yield ramps toward 11–16%, you're at 20–64%. the cheap borrow is a function of low utilization though. as people loop in it climbs and the spread tightens, so the edge is front-loaded. but overall really excited by this, lets build loop sizing as CLI?
a11 tweet media
English
0
0
6
2.5K
liquid 💧
liquid 💧@_proxystudio·
wstETH vs wstDIEM leveraged yield farming #'s wrapping & staking ETH on lido (for example) and depositing it to morpho to lev yield is one of the most dependable and liquid lev yield farming strategies in defi. people frequently loop wstETH 10x to achieve a 5-8% APR 10x lev for 5-8% (!) APR as we scale $DIEM supply on @Morpho, wstDIEM depositors will be able loop only 3.3-4.4x for ~27 - 56% APR 4.4x leverage for up to 56% APR (!!!!) in other words: a wstDIEM leveraged yield farming strategy accrues up to 7x the yield (56% APR) at less than half the leverage of a typical 10x wstETH position earning at most 8% APR. On top of that, the source of yield is daily demand for discounted inference vs inflationary tokenomics there is currently $100k of $DIEM in the wstDIEM vault, the vast majority of it provided by us. liquid, earning liquid builds inference markets. $liq
liquid 💧 tweet media
English
10
5
54
10.6K
vukan (blkn/acc)
vukan (blkn/acc)@vukan0x·
recording of my talk at the inaugural @MetaDAOProject owners meeting. i cover: - tokenizing dinosaurs @JurassicFi - how to turn motion into commitments - tips for founders wanting to raise - the upcoming Deaton raise enjoy!
Colosseum@colosseum

Today, Colosseum will host the inaugural MetaDAO Owners Meeting in San Francisco. Builders, founders, and investors from across the world have flown in to worship at the altar of decision markets, including futarchy’s inventor, @robinhanson. Doors open at 10am.

English
8
5
55
4.8K
a11
a11@StreamVC_·
@sudoingX it is full price war, and commodity price wars are always won by whoever's paying the bill.
English
0
0
1
59
Sudo su
Sudo su@sudoingX·
what a move by elon. everyone else spent two years scraping github for code, xai went and trained the model with cursor, directly inside the place where real engineering happens. distribution and training data in one deal. and the economics are the actual announcement. $2/$6 per million tokens, ~4x fewer output tokens than opus on the same swe-bench tasks, 80 tok/s. the intelligence board is honestly mixed, it takes terminal-bench and deepswe off opus 4.8, drops swe-bench pro and multilingual. elon himself called it opus 4.7 level. that's the right read. but here's the thing, if opus level intelligence gets this cheap, the calculus changes. grok 4.5 takes the everyday agent work and my claude code subscription quietly turns into a fable subscription, the button i press when the task is actually hard. the coding model war just became a price war. price wars are won by users.
Sudo su tweet media
SpaceXAI@SpaceXAI

Announcing Grok 4.5, our first model trained specifically for coding and agents. It was trained with Cursor and offers frontier intelligence at leading speeds and cost efficiency. x.ai/news/grok-4-5

English
5
1
59
4.5K
a11
a11@StreamVC_·
i increasingly think the LLM industry is becoming the telco industry. the model is the network now - a commodity you don't win on. price per minute turned into price per token, the phone subscription turned into the api plan, and the provider you pick comes down to rates and the product around them. same industry, units renamed.
English
0
0
3
85
a11
a11@StreamVC_·
@finkd Let’s Spark it!
English
0
0
1
12
Mark Zuckerberg
Mark Zuckerberg@finkd·
(1) Today we're releasing Muse Spark 1.1 -- a strong agentic and coding model at a very low price. It's available through our new Meta Model API and in Meta AI.
English
5.2K
3.6K
45.4K
23M
a11
a11@StreamVC_·
@ivanfioravanti @cursor_ai the speed difference grok vs opus is crazy - very hard, almost impossible to go back to opus now.
English
0
0
1
123
Ivan Fioravanti ᯅ
Ivan Fioravanti ᯅ@ivanfioravanti·
Grok 4.5 is powerful, fast and furious! I gave it this basic prompt: "Create A weather dashboard with animated states." Look at the final result! Connected to real APIs! Back to @cursor_ai thanks to this model and upgraded to Pro+ too, to start pushing on it! 🚀
English
11
0
40
20.4K
a11
a11@StreamVC_·
bye bye opus, hello @grok
a11 tweet media
Norsk
0
0
3
84
a11
a11@StreamVC_·
ran into exactly this week. dflash's block verify isn't fused on metal like it is on cuda, so the wider you draft the more the speculative gain gets eaten - full-block was slower than plain decoding on my m5 even cold. and mlx-dspark can't do MoE targets yet either. custom metal kernels are exactly the gap you're pointing at.
English
0
0
1
57
Ivan Fioravanti ᯅ
Ivan Fioravanti ᯅ@ivanfioravanti·
There is a lot of potential in Apple Silicon devices, not yet unleashed. Custom Metal Kernels combined with powerful LLM will help here 💪
English
12
2
81
7K
a11
a11@StreamVC_·
@sudoingX qwen3-coder-30b-a3b at 4-bit on an m5 air, 32gb. i serve it through my own macprovider-cli so it's always up. it's a 30b MoE but only ~3b params fire per token, so it decodes fast enough to leave running all day and still codes like a 30b. that's my daily.
English
0
0
1
221
Sudo su
Sudo su@sudoingX·
what's your go to local model right now? any hardware counts. whatever you've got. just tell me what you actually run daily.
English
144
1
115
37.5K
a11
a11@StreamVC_·
correction on my own post. went back through the per-trial data and the run order skewed the dflash line. dflash ran last, after the other modes had already heated a fanless air, so it was throttling while baseline got the cold slot. on the cold trials dflash cap=2 actually beat baseline - 16.9 to 21 tok/s vs 15.6. the flat median was a thermal artifact. what still holds cold: full-block dflash lost, so drafting that wide on a small 8-bit target is the wrong regime. and dspark won every workload, hot or cold. re-running with cooldowns between modes and a real draft-cap sweep before i trust the dflash-vs-baseline comparison.
English
0
0
2
54
a11
a11@StreamVC_·
nvidia's showing dflash as the local inference win. i ran it on an m5 air and dflash was the slowest option i tried. qwen3-8b at 8-bit via mlx-dspark, baseline 15.6 tok/s. dspark took code/math to 24.5, a clean 1.58x. dflash barely moved it at a draft cap of 2, and full-block dflash fell to 12.5 - slower than plain decoding, even though it accepted more tokens per step. the cost is in the verify. on a small 8-bit target the weight read is short, so verifying a wide block isn't free the way speculative decoding assumes - it costs real compute, and dflash drafts the widest block. dspark's narrower draft stayed under that line and won every workload. (3-run medians, dense qwen only, no moe yet - chart + full numbers below)
a11 tweet media
NVIDIA RTX Spark@NVIDIARTXSpark

Local AI just got faster. ⚡️ We worked with @ggerganov to add DFlash support in llama.cpp delivering ~2x faster inference.

English
1
0
3
210
a11
a11@StreamVC_·
ran the apple-silicon side on an m5 air via mlx-dspark. qwen3-8b 8-bit baseline 15.6 tok/s, dspark 1.58x on code/math, but dflash regressed here - cap=2 barely moved and full-block was slower than baseline despite higher acceptance. small target + modest bandwidth makes the wide verify not pay off. dense only, haven't tried a MoE target yet.
English
0
0
1
85
Georgi Gerganov
Georgi Gerganov@ggerganov·
llama.cpp recently added DFlash support to its speculative decoding arsenal. Along with MTP, Eagle3 and various ngram-based techniques, the local model performance takes another step up. Special thanks to NVIDIA team and Ruixiang Wang specifically for leading this effort! github.com/ggml-org/llama…
English
17
48
399
80.3K
a11
a11@StreamVC_·
@cocktailpeanut Interesting concept. I think MacProvider could extend this into an idle-compute layer for Apple Silicon: one-click provider onboarding, automatic model/capacity tuning, local reliability checks, team/device pools, and routing jobs to Macs based on price, latency, and model fit.
English
0
0
0
19
cocktail peanut
cocktail peanut@cocktailpeanut·
More details on "Home Server": 1. PEER NETWORK When you run Pinokio on multiple machines at home (ex: macbook, windows PC, etc.), they will automatically discover one another and create a LAN peer network. And you can open them with 1-click.
cocktail peanut tweet media
Pete@MidiPunk

@cocktailpeanut This is great thank you! Been trying to access through LAN for longer than care to admit, could always connect to Pinokio but not running apps.

English
3
2
31
2.5K
a11
a11@StreamVC_·
@cocktailpeanut Love Pinokio concept! Landed my MacProvider on Pinokio and will keep iterating to make it part of Pinokio ecosystem. Host open models on idle Apple Silicon - one-click setup for turning a Mac into a paid local inference provider. github.com/Augustas11/mac…
English
0
0
0
18
cocktail peanut
cocktail peanut@cocktailpeanut·
Introducing Pinokio 7 P7 is here, and it is all about AI agents. 1. Interpreter: Let AI agents control your apps, with zero config 2. Assistant: Talk to your apps in chat 3. Memory: AI agents remember your chats around each app, so you can pick up where you left off
cocktail peanut tweet media
English
30
24
201
36.2K
a11
a11@StreamVC_·
"we don't need anthropic anymore" is what a lot of good teams have been privately testing and staying quiet about. the frontier model became a landlord. great product - until the day the price moves or the model you built on gets deprecated, and your roadmap suddenly belongs to them. comma said it first with their name attached. half of my feed already agrees but won't admit it yet.
comma@comma_ai

Goodbye @AnthropicAI. All comma dollars go towards the mission and our values. Our dollars come from customers buying hardware they own. And we spend them on GPUs and engineers to write MIT-licensed software.

English
0
0
3
104