Eagle Farm Race1 no16 Bengal Diamond, Big run in listed grade last start, way down in class draws well and good record this track Race2 no4 Lyles, 1st up and trial was super, looks too good for this lot Race3 no2 Royal Supremacy, looks ready to peak 3rd up, gets Zahra and well placed this class Race4 no2 Blind Raise, looks a serious horse winning at both starts, gets Nash and the one to beat for sure

YOLOPARK is heading to Tokyo Toy Show 2026! Booth No.: 4-28 Date: August 27–30 Venue: 3-11-1 Ariake, Koto-ku, Tokyo 135-0063, Japan West Hall 4 Come visit the YOLOPARK booth and get a closer look at our latest collectibles and surprises! Stay tuned to our social media for more event updates! #YOLOPARK# #トランスフォーマー# #東京おもちゃショー# #transformers#

I don't know if Jev will win, but I do believe the architecture & benefit is here to stay. So I found someone actually shipping with Jev's architecture and made him share best use cases. @moritzkremb (ex-PM, Prompt Warrior) took me from easy mode to god mode for a 13-minute masterclass on Jev. 1) Easy Mode: what Jev actually is - Definition: a classification / decision model. You give it input + a question. It returns probabilities (yes 96% / no 4%), not an essay. You need two building blocks: - Inputs. The thing you’re judging (an ad creative, a resume, a page state, pasted invoice text). - Checkable questions. Dozens if you want. Fire them in parallel. Get instant yes/no (with confidence) back. - Why it matters: ~20–200x faster and ~40–400x cheaper than making an LLM write you a paragraph for every judgment. Use case: Meta Ads Analyzer. For every ad, Jev runs a question pack (does the hook call out the audience? quantified result in the first sentence?) and turns those probabilities into a dashboard you can act on. 2) Hard Mode: detailed scoring across a pile of items Definition: stop asking one model for a vibe summary. Ask many precise questions across hundreds of items, then bucket the results. Rules of thumb: - Write questions like hiring criteria or ad QA, not vibes. “Has the candidate shipped and maintained a user-facing TypeScript/React app?” - Weight what matters. Not every must-have is equal. - Sort into clear buckets (Shortlist / Review / Decline) so humans only spend time on the middle. Use case: An AI recruiter that sifts hundreds of applicants in milliseconds against your must-haves. 3) God Mode: real-time products that didn’t make sense before Definition: Jev as System 1 (fast, intuitive) next to LLMs as System 2 (slow, deep reasoning). Speed + cost unlocks products that were too slow or expensive to build. What to use Jev for: - Lots of data that needs detailed checks → classification loop. - Must react while the user is still speaking or pasting → hit Jev on every word / state update. - Combine them. LLM for deep reasoning. Jev for the snap decisions inside the loop. Use case: voice-controlled browser (transcript + page state → click/scroll probabilities) and smart paste (invoice text maps into the right expense fields as you paste). Full episode:

Toronto, we’re back on July 25 for round 2 of the RaveDAO Community Series. 🪩 A night built for the unexpected. From Osheyack's raw fusion of gabber and techno to Endgame's abrasive synths, alongside Toronto's boundary-pushing local artists exploring broken beats, polyrhythms, and sounds beyond the grid. No 4-on-the-floor. No rules. Just music that moves differently. Come shape the sound and energy with Toronto's community. 🕙 July 25 | 11 PM–4 AM 📍 70 Huron St, Toronto 🎟️ Tickets via @plvrio: