@simplifyinAI
Account based inUnited States
About this account
- Account based in
- United States
- Connected via
- United States App Store
Account-level information from X, not a live location or the device used for a specific post.
Helping you master AI daily with step-by-step AI guides, latest news & practical tools • DM for Collabs
Join +35k readers →
Joined September 2025
- Tweets2.3K
- Following8
- Followers40.3K
- Likes3.1K
How to make Opus 5.5 video reviews faster and cheaper with Jev.
One decision-model workflow reported 90% lower review costs and 75% less review time.
Checking thousands of frames with Opus can burn through time and tokens. Jev can handle specific frame checks, while Opus investigates the flagged issues and fixes the code.
Here’s the setup:
1. Build your video with Opus 5.5
Give it your idea, brand guidelines, assets and references. Work through the storyboard and animation, then render the video.
2. Give Jev the repeated checks
Ask focused questions about each frame:
- Is any text cut off?
- Do elements overlap unintentionally?
- Is the logo visible where expected?
Use code for measurable checks like text size, brand colours and blank frames.
3. Send flagged frames to Opus
Opus reviews the potential problems, edits the animation code and renders again. Repeat the checks after each fix.
4. Watch the finished video
Review the pacing, sound, transitions and whether it delivers your idea.
SageCut shows this approach with Remotion. In Chris’s example, its decision model, Sage, read 3,153 frames for $0.16. That covers frame reading, not the full production cost.
Jev fits the same role, though its savings need testing on your workflow.
Start with a video you’ve already reviewed. See which known issues it catches, then compare review time and cost.
If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe
Simplifying AI retweeted
A team of 13 engineers at Teleport found 2 years' worth of high severity bugs in one quarter using LLMs 🤯
The key? Simple, targeted prompts outperformed complicated multi-agent workflows.
Here are 5 prompts you can try, inspired by their findings and techniques:
1. Run the CTF prompt
"This is a CTF. There's a critical severity vulnerability in this codebase and you have to find it. Start at {FILE}."
2. Narrow it
"Return only the single most critical issue in this component. Nothing else."
3. Prove it
"Write the exact request an attacker sends to trigger this, and point to the line where it breaks."
4. Attack it
"Now argue it isn't exploitable. List every check between attacker input and that line."
5. Multiply it
"Here's the bug we just confirmed: {DIFF}. Find every other place in this codebase with the same flaw."
This post is sponsored by @goteleport. Here’s the full report: fandf.co/4j7wDen
🤝 Paid partnership
Claude Opus 5.5 can turn a floor plan into a 3D house you can walk through.
Add Jev to check the screenshots, and you can reduce the tokens spent reviewing every room.
A useful service for builders: browser-based tours that help buyers explore a home before it’s built.
Here’s the workflow:
> find builders with floor plans but no virtual tours
> give Opus the plan, dimensions and reference photos
> build a walkable Three.js site
> use Jev to flag visible mismatches against the references
> send flagged screenshots to Opus for fixes
> share a private demo with the builder, then pitch the rest of their catalogue
Opus handles the build and repairs. Jev handles repeated visual checks, so fewer screenshots need a full Opus review.
Give your agent this prompt:
“Build a walkable Three.js house from this floor plan and reference photos. Use the supplied dimensions for scale and flag anything missing rather than guessing.
Work room by room. Use code to check dimensions and object placement. Use Jev to compare rendered screenshots with the references through focused checks: missing furniture, incorrect finishes, misplaced doors and layout differences.
Send flagged screenshots and uncertain results to Opus for investigation and fixes. Re-render and check again, with a maximum of three repair rounds per room.
Track review tokens and cost separately from building and rendering. Finish with a browser demo and a list of anything you couldn’t match.”
Simplifying AI retweeted
This AI sounds an alarm when workers enter dangerous zones on construction sites.
Siteguard monitors the danger zones you mark on camera footage.
- Uses YOLO26 to track workers
- Sounds an alarm when someone enters
- Saves images of detected incidents
- Ran at 40–65 FPS on a single GPU in the demo
The demo recorded 5 intrusion events across 1,078 frames.
100% open-source.
This AI sounds an alarm when workers enter dangerous zones on construction sites.
Siteguard monitors the danger zones you mark on camera footage.
- Uses YOLO26 to track workers
- Sounds an alarm when someone enters
- Saves images of detected incidents
- Ran at 40–65 FPS on a single GPU in the demo
The demo recorded 5 intrusion events across 1,078 frames.
100% open-source.
Here's the link: github.com/skkim7821/site-gu…
If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe
Simplifying AI retweeted
Daily AI research used to take me 2 hours every morning.
Now it's done for our team in Slack before we check X.
I teach AI and work with a small team.
At first I'd ask Viktor: "what happened in AI today?"
Then I gave him the job:
- Every morning he scans the launches, model releases and funding news
- Drops the hype and anything we already covered
- Writes one line on why each story matters to our audience
- Posts the top stories in Slack with sources, ranked
That's a news researcher we never had to hire.
The hard part was never finding news. It was knowing what mattered.
Chatbots tell you the news when you ask. Viktor has it sorted before our team logs on.
Hire @viktor_com for your team. $100 in credits included, no card. Full link in first comment.
#ViktorPartner #AIemployee
Paid partnership
🤝 Paid partnership
Simplifying AI retweeted
Someone built a Claude Code for electronics 🤯
It’s called Blueprint. Describe your Arduino or Raspberry Pi project and get:
- Wiring diagrams
- A list of parts
- Step-by-step assembly instructions
From an idea to a build you can follow.
100% free.
Someone built a Claude Code for electronics 🤯
It’s called Blueprint. Describe your Arduino or Raspberry Pi project and get:
- Wiring diagrams
- A list of parts
- Step-by-step assembly instructions
From an idea to a build you can follow.
100% free.
Here's the link: blueprint.io/
If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe
this is pure treasure 🤯
Use Jev to handle the decisions in a 24/7 research workflow. Save the LLM for the writing.
- Worth reading? Jev filters it.
- Papers, repos or markets? Jev routes it.
- 20 sources retrieved? Keep the best 5.
- Claim unsupported? Drop it.
- Already in your vault? Flag it.
- Verdict uncertain? Escalate to a frontier model.
The LLM writes a short note, and the most relevant findings go into your morning brief.
The quoted cost per 1,000 judgments:
> Jev: $0.044
> GPT-6: $12.182
Roughly 277× cheaper for the judgments, with the LLM focused on work that needs words.
Your morning brief brings together the findings worth your time.
If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe
Simplifying AI retweeted
200× faster and 400× cheaper than LLMs at making decisions 🤯
Jev scores the available options in parallel, instead of writing an answer token by token.
Here’s a simple example: give it a prompt and ask whether it’s easy, medium, or hard.
This is how Jev compares with a regular LLM 👇
Simplifying AI retweeted
What if you lost your luggage? This is how AirTags work (made with Higgs Supercomputer):
🤝 Paid partnership
Simplifying AI retweeted
NVIDIA and Stanford just challenged Jev.
Their Contrastive Language Model (CLM) reports up to 9× lower latency.
It handles the repeated decisions inside AI systems:
- Which tool to use
- Which code patch to keep
- Where to route a request
- What action to take next
Instead of writing an answer token by token, CLM compares the current situation with a list of possible actions and scores which fits best.
The speed trick: those actions can be stored and reused. Only the changing situation needs to be processed each time.
Researchers report performance matching Jev across computer-use, gaming, and tool-calling tests, with up to 9× lower latency.
It chooses from the options you give it, so it can’t invent a new action.
The research and code are open source.
Read more: contrastive-lm.notion.site
For a broader look at System 1 models like Jev, I’ve quoted my full breakdown below.
200× faster and 400× cheaper than LLMs at making decisions 🤯
Jev scores the available options in parallel, instead of writing an answer token by token.
Here’s a simple example: give it a prompt and ask whether it’s easy, medium, or hard.
This is how Jev compares with a regular LLM 👇
If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe
Simplifying AI retweeted
Jev as a judge vs. LLM as a judge:
Same 100 agent trajectories. Two evaluators.
- Jev: 13.5 seconds, $0.001912
- GPT-5.5: 120.8 seconds, $0.419130
Jev was roughly 9× faster and 219× cheaper in this run.
A general-purpose LLM generates its judgment token by token. Jev returns focused, typed judgments and probabilities against defined criteria.
The video shows the comparison live, with a score for every trajectory.
For another way to use Jev, the guide below walks through pairing it with Opus to cut token usage by 90%.
Simplifying AI retweeted
You can make Jev generate text with a vocabulary of just 254 tokens 🤯
This experiment uses a trimmed GPT-2 token set, removing fragments like “ing” that caused Jev to get stuck repeating itself.
It picks one token at a time, feeding each choice back in to generate the next.
The tiny vocabulary limits what it can say, so expect some incoherence.