9/17/2026 Daily Recap: The Bulls second wind? - NAAIM came in at 72 today so we have some money on the sidelines if this is going to grow some legs. - $MAGS closing right into all time highs, this group looks like it can start to lead from here. - $ARKG incredible trend continues. $TXG $TWST $HG $MRNA $TEM - Refiners non stop daily highs $VLO $DINO $PSX $MPC. Select shippers doing the same $ECO $TNK $INSW $STNG $FRO - Semiconductors with some fight. $SMTC $QRVO $AMD $INTC. - Memory names find a bid along the 50sma. $MU $SNDK. - Software names shaping up well $PLTR $CRM $TEAM $NOW. - $DELL $HPE $HPQ still with plenty of strength! - Cyber names gap down but find a bid back into new highs. $CRWD $OKTA $PANW $NET $RBRK - One day at a time but you have to be encouraged by the resilience of this market. - In an ideal world this chop ends soon, but before jumping for joy I still need to find myself in more names.

everyone seems to calculate tokens per second window differently A. time from request start to response end B. time from first token coming back to end of response i think it should be A (so it ecompasses TTFT), wdyt?

100% AFFINITY (without wex or gore) 86% WITHOUT AGITATOR 100% MAX MIGHT UPTIME VIA SECOND WIND FREE ZOH REGEN WITH MEDICINE 3 AND RECOVERY 3 GOOD SHARPNESS AND DAMAGE MAX OUT BURST THIS IS IT HOLYYYSHIIIIIT

ROSIE CAN'T QUIT TALKING ABOUT TRUMP The boys of @lfs6b have some thoughts after @BeniRaeHarmony reports Rosie O'Donnell declared she's “refamous” and credited her feud with President Trump for giving her a second wind. “She just wants attention... She’s just obsessed.” @slickricksports @damonroberts

Elon Musk unveiled the Tesla Cybertruck in November 2019 and it became the most famous product launch failure in automotive history. Because the "unbreakable" windows shattered on live television in front of the entire world. Franz von Holzhausen, Tesla's head of design, was supposed to throw a metal ball at the driver window to demonstrate the armored glass. In rehearsals it worked perfectly. The glass held. The demonstration was supposed to be the dramatic highlight of the presentation. On stage Franz threw the ball. The glass shattered into a spiderweb of cracks. The audience gasped. Elon stared at the window. He said "oh my god." Then he told Franz to try the rear window. Franz threw the ball again. The second window also cracked. Elon stood on stage for the remaining 15 minutes of the presentation in front of a truck with two shattered windows. He did not leave the stage. He did not cut the presentation short. He continued presenting features and specs as if the most embarrassing product demo in Tesla's history was not sitting behind him in full view of every camera in the room. The stock dropped 6% the next day. The internet produced a million memes. Every automotive journalist wrote that the Cybertruck was dead on arrival. Then Elon tweeted the behind the scenes video of the rehearsal where the glass held perfectly and implied that the stage demo stress tested the glass beyond the rehearsal conditions. Within a week the Cybertruck had received over 250,000 pre orders. More than any truck launch in history. The broken windows generated more attention than a perfect demo ever could have. The failure became the marketing. The man who stood on stage in front of his most public failure and refused to flinch turned embarrassment into a quarter million orders.

im going to let sf men in on a crazy secret, and if you follow these instructions exactly, you'll be able to date any girl you want 1. most guys just dont have the balls to talk to women in public 2. if you have the confidence to approach a woman in public, you're disrupting the normal daily interaction, and you have a 30 second window in space and time to connect with this girl who is standing in front of you 3. if you are a base level attractive and some combination of enjoyable to be around, smart, and high status, almost any girl in sf will give you a shot 4. most women in sf are desperately seeking a partner, so we're in a men's market

Forget killing cancer cells. South Korea just figured out how to talk them back into being normal. Scientists at KAIST in Daejeon have done something the world has been chasing for decades. They found a molecular switch that flips cancer cells back into healthy cells. No chemo. No radiation. No destroying anything. Just… reversal. Professor Kwang-Hyun Cho and his team caught cancer in the act. That tiny window where a normal cell is on the edge of turning malignant but hasn't fully crossed over yet. They call it the "critical transition" — the same kind of jump that happens when water hits 100°C and becomes steam. In that split-second window, the cell is unstable. Normal and cancerous at the same time. And that's exactly where they hit the switch. In colon cancer trials, they targeted three master genes — MYB, HDAC2, and FOXA2 — and the cancer cells didn't die. They went back to being healthy intestinal cells. Like nothing ever happened. The team built a digital twin of the gene network to map every move a cell makes on its way to becoming cancerous. Then they reverse-engineered the path home. Their paper landed in Advanced Science, published by Wiley. It's still early. Lab trials and mice. Human treatment is years away. But the idea of curing cancer without killing a single cell is no longer science fiction. Source: KAIST (Korea Advanced Institute of Science and Technology), published in Advanced Science journal

I just launched an AI model based on sleep data… and it accurately predicts your age. I teamed up with @m_franceschetti, and it's now available on their platforms. It turns out you have a sleep fingerprint. This research can identify you out of thousands of users from one night's signal with 92.5% accuracy. It also detects… + biological age within 3.3 years + diabetes better than Apple’s model + speed of aging + heart failure at 0.822 This Eight Sleep model is possibly the most accurate contactless bioage estimate ever reported. What we did: #1# What data was it built on? This model was built on the largest raw biosignal dataset ever used to train an AI, from any device, including every wearable on the market. > 2.04 million hours > 136,575 participants > 498k sessions > 122 million segments #2# What can it predict? It can predict your biological age (the age your body acts like) within 3.3 years. It also predicts heart-related and metabolic conditions. Here are the detection scores (AUROC): > diabetes (0.852) > heart failure (0.82) > hypertension (0.810) > sleep apnea (0.792) > snoring (0.751) > general heart conditions (0.734) > cancer (0.678) > hot flashes (0.671) > migraines (0.673) #3# How was it built? Interestingly, the pretraining task was not “predict someone’s age”. The model was tasked with comparing two 60 second windows across different nights to figure out if the nights belonged to the same person. To do that, it had to find someone’s ‘sleep fingerprint’. These are biological signals that the data is coming from the same person. Things like how forcefully your heart contracts, your breathing depth and rhythm, and the timing of the recoil wave each heartbeat sends through your body. Those signals are age-predictive. It learned to estimate age, detect diabetes, and flag heart failure as a downstream readout. The whole pretraining run was ~four days. #4# Why is it good at age? The reason aging prediction is accurate is mechanical. Aging stiffens arteries, reduces cardiac compliance, changes autonomic tone, HRV declines. Aging also alters sleep architecture. Deep sleep shrinks and fragmentation rises. Every one of those changes the recoil waveform and its overnight dynamics. Said differently, the heart of a 65 year old mechanically pushes the body differently than a 25 year old's. #5# Why a bed vs wearables? A bed is an elegant solution. It makes a high fidelity uninterrupted 5 to 10 hour recording every single night possible. And session-level sequence modeling becomes viable. Whereas wearables get fragmented data: battery limits, sparse snippets, people taking the watch off, adherence dropping over weeks. #6# More data, better prediction The bigger the training batches (the more people the model compares at once) the better it got, log-linearly (R²=0.982). That means the recipe is predictable: you can forecast improvement with more compute, the same way scaling laws work for language models. The current model only ever compares two nights at a time, and the average training user contributed under 4 nights. The team's stated next step is modeling 30+ consecutive nights per person. You can imagine how this will improve with the constant stream of data Eight Sleep gets every night. — It’s worth noting some limitations. Internal labels are self-reported and external cohorts are small, and that this is a research milestone, not a diagnostic device. What makes this exciting: a passive, daily activity like sleep can now provide meaningful insight into your well-being. I speak often of Autonomous Health, a world where the things around us take care of us without our knowing or asking. Eight Sleep is a great example of this in practice and a major reason I maintain so much optimism for the future of health.

Requiem for building in public Music video workflow + Prompts: One song, a simple story, singers across 4 locations, a full edit, captions burned in. Models used: Suno for the track, Seedance 2.5 for the story footage, MiniMax H3 for the lip synced singing, faster-whisper for word timing, Claude to edit, ffmpeg to burn captions. THE SONG (Suno) Short lyrics, fast beat, vocals on second 0. Long slow AI songs fall apart because the model has nothing to hide behind. Fire small batches, 2 clips at a time, and listen before firing more. Write every new attempt from zero. Stacking "less this, no that" onto the last try feeds the model your confusion and hands it back. One adjective moves everything. I put "soft" in a prompt once and the whole vocal switched to a woman. Remix prompt (paste into Suno style box): aggressive male rap, hard boom bap drums with fast energy, dark piano loop, deep male voice on every line including the hook, punchy mix, vocals start immediately at 0:00, no instrumental intro Lyrics: [Hook] It's just this thing I feel When I wanna steal It's just this thing I feel When I wanna steal [Verse 1] Yo, I see you on X, all over my feed You're building in public, I'm watching you build Your MRR chart looks like a hockey stick I screenshot it sometimes, that's normal right [Hook] It's just this thing I feel When I wanna steal [Verse 2] I learned a lot from you I think I deserve it too So I copied everything from you Same landing page, same pricing, same font And now you blocked me What happened bro I was your biggest fan [Hook] It's just this thing I feel When I wanna steal Tip: short lyrics, fast beat, vocals at 0:00, fresh prompt every round. THE STORY Think old MTV. The video is the movie this song is the soundtrack of. Keep the plot dead simple, something you can follow with the sound off. Mine: a broke founder copies a guy, dreams he is rich, wakes up, sees he got blocked, spits his cereal at the screen. That is all of it. Tip: if you cannot explain the story with zero words, cut it down. THE STORY FOOTAGE (Seedance 2.5) Seedance made the apartment story as one 30 second clip from reference images. Two things kill Seedance: Too many object interactions in one shot, and timestamps like "0 to 4 seconds" which it reads as a time lapse and speeds through. plain shots labelled "Shot 1, Shot 2" at natural speed will do the trick For the hard beat at the end, a guy waking up, eating cereal, seeing a screen, then spitting milk on the lens, text alone will not hold it. I built a 3 panel storyboard image and fed it as a reference. In the prompt you tag that image at the exact moment it happens, tell it the board reads left to right, describe it once, and move on. The rule that saved it: chronological order, tag each image where it belongs in time, say everything a single time, never repeat a thing. Repeat one detail twice and the model fixates on it and breaks the shot. Tip: for anything complex, hand it a storyboard picture and describe it once, in order. THE SINGING (MiniMax H3) H3 is the model that lip syncs to your actual track and keeps it. Seedance cannot, it regenerates its own audio. In H3 you attach your audio slice, set it to copy, and the mouth follows your real song. H3 caps around 15 seconds a clip and the song is 43. So I cut the song into 4 windows of about 11 seconds and generated a shot for each window. Then I did it across 4 locations, subway, warehouse, empty office, street. That is a 4 by 4 grid, 16 clips. I added 8 more where the whole crew sings and dances. Around 24 short singing clips to cover a 43 second song. You are building a bank of clips to cut from. Small H3 rules that matter: the audio slice must be a touch shorter than the clip, name every speaker, and compress the slice so there are no silent gaps for the model to fill with invented sound. Tip: chop the song into sub 15 second windows, shoot each shot per window, build a clip bank. THE EDIT (Claude) This is where most people lose hours. I made editing fast by doing the prep once. Every clip gets normalized to the same size and frame rate up front. After that each edit is a single ffmpeg pass with no re-encoding loops. The base layer is the song. Every clip's own audio is thrown out. To keep mouths in sync I gave the editor the math: each clip knows which second of the song its first frame belongs to, so to place it at song second S you trim it to start at S minus that offset. I also handed over word level timing from a whisper pass so cuts could land on real lyric moments. The rules I gave: nothing stays on screen too long, pace every cut to the lyric and the beat and what is on screen, never put two shots from the same location back to back, keep it heavy on story B-roll, never reuse a frame. If the cut feels like a metronome you failed. If it feels random you failed. Then the actual move. I did not ask for one perfect edit. I gave 7 agents the same rules and the same clip bank and told each to cut the whole thing its own way with a different emphasis. 6 came out flat. 1 landed around 90 percent. I finished that one by hand in CapCut. Tip: give strict rules plus the timing data, generate many full edits, keep the best and finish it yourself. CAPTIONS (ffmpeg) Burned straight from a styled subtitle file with ffmpeg. Seconds, not the long render a motion tool costs. The words come from the real lyrics, the timing comes from a whisper pass on the audio, and it highlights the word being sung. Big, thick, one pop color on the active word. Tip: real lyrics for the words, whisper for the timing, burn with ffmpeg. The AI did not make this video. I directed it, generated in volume, and kept the best takes. That is the whole game right now.