launch videos are becoming a standard part of startup marketing, and andcut is basically a reference library for them.

it collects product, startup, and feature launch videos from X, YouTube, LinkedIn, etc., then makes them searchable by company, launch type, stage, platform, format, industry, and duration. small but useful if you’re trying to figure out how to frame your own launch without starting from a blank page.

🔗 https://www.andcut.video/
🔗 https://x.com/adilatwork/status/2074876337098289243

Meta is opening up Muse Spark 1.1, its new agentic/coding model, through the Meta Model API public preview. the interesting part is not another chat model, but that Meta is packaging long-context, tool-using, computer-use, coding, and multimodal work into one developer-facing API.

Meta says it can manage a 1m-token context, delegate to subagents, use desktops and browsers, and work with images, video, and docs. the eval claims put it near frontier models, but the part to watch is whether the API holds up for real agent builders outside Meta's own tests.

this is surely going to get washed away by 5.6Sol which drops later in about 45 minutes! (1am sgt)

🔗 https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/

official model card/deets are out!:

OpenAI launched GPT-5.6, a new model family with Sol as the flagship, Terra as the lower-cost everyday model, and Luna as the fastest budget option. the interesting part is not just benchmark lift, but the product shape: better coding and knowledge work, programmatic tool calling, and an ultra mode that coordinates multiple agents for harder tasks.

pricing starts at $5 / $30 per 1M tokens for Sol, $2.50 / $15 for Terra, and $1 / $6 for Luna. it is rolling out across ChatGPT, Codex, and the API, with OpenAI also saying GPT-5.6 gets stronger safeguards for cyber/bio work and more trusted-access controls for sensitive use cases.

🔗 https://openai.com/index/gpt-5-6/

OpenAI is turning the Codex app into a broader ChatGPT desktop app, putting Chat, Work and Codex in one place. the desktop rollout covers every plan, including Free.

the new ChatGPT Work agent uses GPT-5.6 to pull context from connected apps and local files, handle multi-step projects for hours, and turn them into docs, decks, spreadsheets, Sites and reports.

SO EXCITED!!!!!!!!!!!! (i dont think im sleeping)

🔗 https://x.com/OpenAI/status/2075274271845404744

OpenAI is running Build Week from July 13 to 21, a global online hackathon where you can use Codex and GPT-5.6 to build an app, website, agent, game, devtool, workflow, or something unexpected. you can enter solo or with a team, with a $100,000 cash prize pool. submissions close July 22 at 8am SGT.

we're running two 🇸🇬 Singapore sessions too: Community Hack Day on July 18, from 11am to 5pm, for an afternoon of building with Codex, followed by Community Demo Night on July 19, from 5pm to 8pm, with open build time, community support, and optional demos. both require registration approval!!!!!

COME JOIN US!!!!!!!!!!!

Singapore Hack Day: https://luma.com/dpc486td
Singapore Demo Night: https://luma.com/6yp0p0pd

🔗 Global details: https://openai.com/build-week/

melius is a creative canvas where you brief an agent, then watch it build a visual workflow across image and video models. instead of jumping between tools and rewriting prompts, you can inspect each step, steer individual nodes, and keep the whole campaign in one workspace.

the interesting shift is from ai tools as generators to ai agents as a visible creative team, with humans directing the workflow instead of prompting one model at a time.

🔗 https://www.melius.com/

inference.net is testing AutoTune, a 25-line SDK that watches an existing LLM workload, distills it into a task-specific 1-30B model, and automatically sends changed requests back to the frontier teacher.

the company says training and evals take around two hours and cost under $250, while routing can cut cost and latency by over 90%. it currently targets single-shot extraction, classification, and summarization tasks, with customer-owned weights. private beta for now.

🔗 https://x.com/samhogan/status/2076044602554159240

prose is a tiny style patch for coding agents: one markdown file that tells Codex, Claude, OpenCode, Pi and Amp to answer in calmer, more natural technical prose.

it doesn’t change what the model can do. it only adds rules for length, structure, tone and when to use bullets, but the author’s side-by-side examples show how much that instruction layer can change the feel of an answer.

🔗 https://prose.ami.rip

Satya Nadella says the real enterprise ai moat won’t be the base model. it’ll be the private learning loop built from a company’s prompts, corrections, evals, traces, and memory.

he calls this the “reverse information paradox”: companies pay for intelligence, then risk giving away the knowledge that makes it useful. his answer is a company-controlled boundary where that learning stays private, owned, and portable across models.

🔗 https://x.com/satyanadella/status/2076323181154230284

ai agents that use the web need more than Playwright. someone still has to run the browsers, preserve login sessions, handle bot detection and make failed runs debuggable.

Kernel packages that infrastructure into on-demand cloud browsers with persistent profiles, live human takeover, session replays and parallel scaling. agents can connect through familiar tools like Playwright or Puppeteer, while Kernel handles the browser fleet underneath.

🔗 https://www.kernel.sh/

superpowered.design is a new curated directory of design tools built with ai agents. it collects small tools for motion, visual effects, design systems, Figma workflows and more, with search, categories and sorting built in.

the launch started with 23 tools and the collection is already being updated through community submissions. a useful place to see what designers are making with agents beyond the usual chat interface.

🔗 https://superpowered.design

Wan has open-sourced Wan-Dancer-14B, a model for generating longer dance videos that follow both a reference character and music. the team reports minute-plus output at 720p and 30 fps.

the catch is hardware: the weights are about 85.7 GB, and the published setup used eight 80 GB GPUs. this is open to run yourself, but not a casual laptop model.

🔗 https://huggingface.co/Wan-AI/Wan-Dancer-14B

did you know that the Amp team now publishes the daily mix of reasoning modes used by everyone (within Amp Code) and by themselves! the chart breaks usage into Low, Medium, High and Ultra, so you can see how often people actually reach for more compute.

it is a small but useful transparency move. the page shows percentages rather than request counts, so read it as a pattern, not a volume benchmark.

🔗 https://ampcode.com/models

fal has released 3DREAL Strong V2, an LTX-2.3 LoRA that turns rough 3D renders into photoreal video while trying to keep the original camera, layout and timing. the weights and a hosted endpoint are available.

the 3d render is really sick - watch the video; to be clear, they rendered the full video above with the 3D render below (from within blender)

🔗 https://huggingface.co/fal/LTX-2.3-3DREAL-LoRA

Google DeepMind CEO Demis Hassabis says human-level AI could be only a few years away. he wants the US to create an independent referee for the most powerful AI models, funded by the industry but overseen by the government. before a model launches, it would be tested for risks such as cyberattacks, biological misuse and bypassing its safety controls.

the idea is that every leading AI company should face the same checks, so nobody can cut corners just to launch first. if a model looks too dangerous, the referee could eventually delay its release or ask the whole industry to slow down. the hard part is making sure the AI companies funding the system do not end up controlling it.

we're inching ever so closer

🔗 https://x.com/demishassabis/status/2076957440109625718

vorflux launched as an “autopilot” for software engineering from former Rippling co-founder and CTO Prasanna Sankar. the company says it has raised a $15m seed to move ai coding beyond copilots that still need constant supervision.

the platform runs your full stack on dedicated cloud machines, splits work across agents and models for planning, building and review, then tests the result in a real browser before opening a pr. the bigger bet is that the valuable layer is no longer code generation, but the system that can reliably take work from idea to merge.

🔗 https://x.com/myprasanna/status/2077069901546852688

Notion can now open Markdown files straight from Finder as formatted, read-only previews, then turn them into editable Notion pages. the desktop app supports up to 10 files at once, which makes reviewing READMEs and notes a lot less awkward.

standard Markdown is supported, but anchor links and tool-specific extensions may need cleanup after import. like freaking finally 💀💀they're really late but ok props to them!

🔗 https://www.notion.com/help/import-data-into-notion

Mint released an MCP server and an open-source Three.js skill pack that let coding agents request 3D assets, pull the finished files into a project and assemble interactive apps or games around them.

the asset generation happens through Mint's remote, credit-based service, while the GitHub repo supplies the agent workflow and Three.js scaffolding. it is a useful pipeline, not a local 3D engine.

this is a project from an indie dev! crazy!!!!!!!!

🔗 https://github.com/mintdotgg/mint-threejs-skills

OpenAI's GPT-5.6 Sol prompting guide is basically an argument for smaller prompt contracts: define the outcome, constraints, evidence, success bar, and stopping conditions, then let the model choose the route.

OpenAI says leaner configs improved scores by roughly 10 to 15% in a sample of internal coding-agent evals while cutting tokens 41 to 66%, but those ranges are directional and should be tested on your own workload.

PS: just ask your clanker/agent to set it up for you! (on codex/or when using 5.6 sol)

🔗 https://developers.openai.com/api/docs/guides/prompt-guidance-gpt-5p6

something is coming for the construction industry!

Monumental raised a $32m Series B led by Khosla Ventures to scale its autonomous construction fleet. it already has 100+ robots laying bricks on real sites across Europe, with work completed for 100+ homes, a school, hotel, community centre and Amsterdam canal walls.

the interesting bit is the business model: contractors hire Monumental for the finished work, not the machines. its Atrium software turns architectural drawings into build plans and coordinates the robots with millimetre accuracy. the funding will expand the fleet, move beyond bricklaying and launch Monumental in the US this year.

🔗 https://www.monumental.co/press/announcing-our-32-million-fundraise

Thinking Machines (mira murati's startup; ex CTO @ OpenAI) released Inkling, a 975B-parameter mixture-of-experts model with 41B active parameters. it was trained from scratch on 45 trillion text, image, audio, and video tokens, supports up to 1 million tokens of context, and ships with the full weights.

the interesting angle is customization: Inkling is available for fine-tuning through Tinker, with native multimodal reasoning and a dial for trading thinking effort against cost.

🔗 https://thinkingmachines.ai/news/introducing-inkling/