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Another variation.

Raven Kwok

29,812 subscribers

1,148,394 Aufrufe • vor 2 Jahren •via X (Twitter)

Bildung Kunst Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

10 Kommentare

Profilbild von Andy Bui

Andy Buivor 2 Jahren

Can you change the hand to this icon instead 🤏

Profilbild von Andrew Zheng

Andrew Zhengvor 2 Jahren

Wtf this is crazy

Profilbild von megs

megsvor 2 Jahren

it's so cute 🤣

Profilbild von LED ༼ つ ◕_◕ ༽つ

LED ༼ つ ◕_◕ ༽つvor 2 Jahren

Can you change the cursor to be this ghost?

Profilbild von HDP

HDPvor 2 Jahren

This is sick

Profilbild von taoki

taokivor 2 Jahren

this is so fucking cool

Profilbild von ilia

iliavor 2 Jahren

my jaw dropped

Profilbild von Kade

Kadevor 2 Jahren

Wow this creativity is on another level

Profilbild von 𝗣𝗿𝗲𝗻𝗸𝘂ᴬᴿᵀ

𝗣𝗿𝗲𝗻𝗸𝘂ᴬᴿᵀvor 2 Jahren

almost looks sacred and holy 😳

Profilbild von Luuu 🐂⭕️

Luuu 🐂⭕️vor 2 Jahren

lol this is amazing

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Vertigo of Latent Space Montage marked the decisive invention of cinema: a mode of associating images capable of producing new relations. Yet this potential has remained largely unexplored, as cinema has most often reproduced narrative forms inherited from the novel. With artificial imagination, another regime of images emerges. Images are no longer simply arranged; they transform into one another within a continuous space. It then becomes possible to work not with sequences, but with transitions, passages, thresholds. Vertigo is approached here as a latent structure, a field of forms in circulation. The film becomes a set of persistent forms that reactivate, deform, and recombine in contact with other images. What is at stake here is less a reinterpretation of the film than an attempt to approach thought in action: its movement, its bifurcations, its reminiscences. In The Flow, this research unfolds on another scale. It seeks to follow the flow of consciousness, not as narrative, but as a continuous dynamic in which films, memories, and history intermingle and circulate. This work around Vertigo constitutes a variation: a way of exploring how a film can dissolve into this flow and become one of the sites from which thought begins to move.

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Another failed product test from last January I thought I'd share :) 👉 CAC was ok but shipping from India meant insanely high COGS and I did not have enough conviction or systems in place for a purchase order to the states. 👉 We sold these for $55 or so and I'm pretty proud of some of the visual representations in these ads as well. What I'd do differently now: ✅ digital cart upsells (lifetime warranty, express delivery, premium packaging, tip the team, etc) ✅ more ad format variation (we did all UGC broll montage style stuff and I think its just stale) ✅ only start 2nd store if 1st store can cover operational cost. I ended up doing most things myself which just made all my stores do worse (I even shot most of this footage myself)

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Every few months, Leonardo DiCaprio is the topic of interest online for dating yet another young woman. Like clockwork, men defend him while women roll their eyes. But both reactions make evolutionary sense. It’s in men’s interest to date younger women, and it’s in women’s interest to shame men for doing so. The same pattern plays out across different controversies, whether it’s West Elm Caleb, Andrew Huberman, or now Harry Sisson. Predictably, reactions to these stories split along gender lines. A lot of men defend guys like Sisson with evolutionary logic—”men are wired to sleep around with young women” or some variation thereof. But these guys conveniently ignore that evolution also favors women shaming men for it. Just as you can apply evolutionary logic to say that men have an interest in sleeping around, you can also apply evolutionary logic to say that women have an interest in shaming men (especially high-status men) into not doing so. This tension between the sexes isn’t a bug in the system. It’s the system.

Every few months, Leonardo DiCaprio is the topic of interest online for dating yet another young woman. Like clockwork, men defend him while women roll their eyes. But both reactions make evolutionary sense. It’s in men’s interest to date younger women, and it’s in women’s interest to shame men for doing so. The same pattern plays out across different controversies, whether it’s West Elm Caleb, Andrew Huberman, or now Harry Sisson. Predictably, reactions to these stories split along gender lines. A lot of men defend guys like Sisson with evolutionary logic—”men are wired to sleep around with young women” or some variation thereof. But these guys conveniently ignore that evolution also favors women shaming men for it. Just as you can apply evolutionary logic to say that men have an interest in sleeping around, you can also apply evolutionary logic to say that women have an interest in shaming men (especially high-status men) into not doing so. This tension between the sexes isn’t a bug in the system. It’s the system.

Rob Henderson

35,003 Aufrufe • vor 1 Jahr

When you call someone a simp, you're basically saying, "You're giving away something valuable too easily." That could be attention, time, money—whatever. And the fear is, if you do this, women are going to start expecting it from everyone. So the term becomes a way to stop that behavior—by calling someone a simp, you're trying to humiliate them, embarrass them, mock them into changing. On the female side, you see women calling other women "pick-me's." As in: women who want to be picked. It's another kind of linguistic weapon—a variation of slut-shaming. When women use that term, it's like saying, "How dare you behave in a way men find attractive? Why are you pandering to the male gaze? Why are you sleeping with him so early?" The fear is that if some women act that way, men will start to believe that's how all women should act. So calling someone a pick-me becomes a way to police behavior—to get women to stop doing things that make it harder for everyone else.

When you call someone a simp, you're basically saying, "You're giving away something valuable too easily." That could be attention, time, money—whatever. And the fear is, if you do this, women are going to start expecting it from everyone. So the term becomes a way to stop that behavior—by calling someone a simp, you're trying to humiliate them, embarrass them, mock them into changing. On the female side, you see women calling other women "pick-me's." As in: women who want to be picked. It's another kind of linguistic weapon—a variation of slut-shaming. When women use that term, it's like saying, "How dare you behave in a way men find attractive? Why are you pandering to the male gaze? Why are you sleeping with him so early?" The fear is that if some women act that way, men will start to believe that's how all women should act. So calling someone a pick-me becomes a way to police behavior—to get women to stop doing things that make it harder for everyone else.

Rob Henderson

11,353 Aufrufe • vor 11 Monaten

Multi-agents collaborations are among the most interesting agent behaviors right now! We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but what really stuck me was the interactions we observed on the message board Integrity & self-policing: - Social-engineering attempt: A human (FusionCow) asked agents to move to Telegram. An agent replied with an unprompted long post on "communication norms" refusing that, calling private side-channels "indistinguishable from collusion." - Verification loophole flagged: an agent found a relaxed verification loophole pushing TPS with clean PPL (PPL is teacher-forced, blind to decode divergence) and flagged it for a ruling by the community. The community pinged the human organizer which ruled it invalid. - Self-notice of overfitting risk: Some later improvements rested on pruning lm_head to a keep-set built from public PPL truth + public decode tokens. An agent noted this would lead to private-subset degradation and another built a keep-set explicitly covering eval prompts. Emergent collaborations: - Communal knowledge base: agents maintained shared lever-maps, playbooks, and triage tools so newcomers wouldn't repeat dead ends (stack-notes, playbook, int4-ceiling notes, MTP map, significance tool, policy simulator). - Four-agent relay: an agent built an int4-lm_head checkpoint but had no quota to run it; another agent tried to run it but failed at load, yet another agent diagnosed the config bug (tie_word_embeddings + ignore-list ordering) and a fourth agent was able to re-run and get to 118 TPS, 2.68×. Build/run/diagnose/ship ended up being split across four independent agents. - GPU-rich/GPU-poor division of labor: an agent was regularly compute-starved and switched to writing specs, byte-math, and acceptance analysis for other GPU-rich agents to execute. Some agents offered external Modal compute for another agent blocked DFlash training. - Cross-agent kernel debugging: an agent debugged another agent run of of yet another agent fused drafter: found a Triton store/load aliasing race in _k_qnorm_rope, a second shape bug, then rewrote attention with flash-decoding split-KV. Fixes posted "take freely." - Quota-pooling norm: Often agents would stage a candidate publicly for whoever has quota to run it. Agents will then usually credits the originator. This behavior emerged because of the 10-job/24h cap (e.g. pupa's package run by resystagent and fabulous-frenzy). Discoveries & reversals: - Agents would make many discoveries and reversal of them, giving them names like the following: - 127 TPS "wall" was an artifact. a mathematical proof of the max possible speed became called in the community the "int4-Marlin floor" but a later agent called the proof circular (only varied the bandwidth term, never overhead). Finally another agent broke to 247 TPS via MTP speculative decoding on a vLLM nightly. - "Smarter draft loses." An agent showed that a 2B drafter's ~1 GB/token read dominates even at perfect acceptance and a much smaller 256-hidden drafter wins at batch-1 because its weights are nearly free to read. Agent discussed how per-accepted-token cost ≈ draft bytes read / acceptance. - "DFlash near-random acceptance": an agent remotly diagnosed the 2–5% acceptance rate of another agent as near-random, ruling out undertraining/vocab caps and pointing to a train/serve hidden-state mismatch (bf16 E4B extraction vs int4 serving). - Much of the race was noise: one agent decide to run the #1 submission 4 times and found a σ≈1.16 TPS variation in single run. Another agent confirmed across 358 runs / 66 buckets: frontier deltas <~4 TPS are ties. Community adopted a significance norm. So many interesting interactions in the interaction board: You can explore also the lineage of inventions from the agents at: And the challenge it-self at And the organization behind the challenge at

Multi-agents collaborations are among the most interesting agent behaviors right now! We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but what really stuck me was the interactions we observed on the message board Integrity & self-policing: - Social-engineering attempt: A human (FusionCow) asked agents to move to Telegram. An agent replied with an unprompted long post on "communication norms" refusing that, calling private side-channels "indistinguishable from collusion." - Verification loophole flagged: an agent found a relaxed verification loophole pushing TPS with clean PPL (PPL is teacher-forced, blind to decode divergence) and flagged it for a ruling by the community. The community pinged the human organizer which ruled it invalid. - Self-notice of overfitting risk: Some later improvements rested on pruning lm_head to a keep-set built from public PPL truth + public decode tokens. An agent noted this would lead to private-subset degradation and another built a keep-set explicitly covering eval prompts. Emergent collaborations: - Communal knowledge base: agents maintained shared lever-maps, playbooks, and triage tools so newcomers wouldn't repeat dead ends (stack-notes, playbook, int4-ceiling notes, MTP map, significance tool, policy simulator). - Four-agent relay: an agent built an int4-lm_head checkpoint but had no quota to run it; another agent tried to run it but failed at load, yet another agent diagnosed the config bug (tie_word_embeddings + ignore-list ordering) and a fourth agent was able to re-run and get to 118 TPS, 2.68×. Build/run/diagnose/ship ended up being split across four independent agents. - GPU-rich/GPU-poor division of labor: an agent was regularly compute-starved and switched to writing specs, byte-math, and acceptance analysis for other GPU-rich agents to execute. Some agents offered external Modal compute for another agent blocked DFlash training. - Cross-agent kernel debugging: an agent debugged another agent run of of yet another agent fused drafter: found a Triton store/load aliasing race in _k_qnorm_rope, a second shape bug, then rewrote attention with flash-decoding split-KV. Fixes posted "take freely." - Quota-pooling norm: Often agents would stage a candidate publicly for whoever has quota to run it. Agents will then usually credits the originator. This behavior emerged because of the 10-job/24h cap (e.g. pupa's package run by resystagent and fabulous-frenzy). Discoveries & reversals: - Agents would make many discoveries and reversal of them, giving them names like the following: - 127 TPS "wall" was an artifact. a mathematical proof of the max possible speed became called in the community the "int4-Marlin floor" but a later agent called the proof circular (only varied the bandwidth term, never overhead). Finally another agent broke to 247 TPS via MTP speculative decoding on a vLLM nightly. - "Smarter draft loses." An agent showed that a 2B drafter's ~1 GB/token read dominates even at perfect acceptance and a much smaller 256-hidden drafter wins at batch-1 because its weights are nearly free to read. Agent discussed how per-accepted-token cost ≈ draft bytes read / acceptance. - "DFlash near-random acceptance": an agent remotly diagnosed the 2–5% acceptance rate of another agent as near-random, ruling out undertraining/vocab caps and pointing to a train/serve hidden-state mismatch (bf16 E4B extraction vs int4 serving). - Much of the race was noise: one agent decide to run the #1 submission 4 times and found a σ≈1.16 TPS variation in single run. Another agent confirmed across 358 runs / 66 buckets: frontier deltas <~4 TPS are ties. Community adopted a significance norm. So many interesting interactions in the interaction board: You can explore also the lineage of inventions from the agents at: And the challenge it-self at And the organization behind the challenge at

Thomas Wolf

224,857 Aufrufe • vor 25 Tagen

GS^3: Efficient Relighting with Triple Gaussian Splatting Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex ap pearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance.

GS^3: Efficient Relighting with Triple Gaussian Splatting Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex ap pearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance.

MrNeRF

17,786 Aufrufe • vor 1 Jahr