In partnership with
Hey,
On 15 September a company called TypeSafe AI released a model that cannot make things up. It also cannot write a sentence, and that trade points at three jobs you are probably still handing to the wrong tool.
Most founders are one system away from turning LinkedIn into their best sales channel.
Engagement is easy to mistake for pipeline. On Sep 30, watch how a founder turns LinkedIn content into real outreach. Live. You'll walk away with a repeatable system: what to post, who to reach out to, and how to sequence it.
Eligible startups also get the LinkedIn-to-Leads Toolkit: ad credits, Apollo, Captions, and HubSpot's Prospecting Agent.
A model that can't write, and can't lie
TypeSafe AI released it in limited early access on 15 September, alongside a $40M seed round led by DCVC. It is not a language model. It produces no text at all. The possible answers are written down before it runs, and it hands back one of them plus a number: how sure it is that the answer is right.
Every answer exists before the model does anything, so there is nothing left for it to invent.
Why it matters: every chatbot you use answers a coin flip in the same steady voice it uses for a fact, and nothing has ever told you which one you were looking at.
3 jobs the pros moved off the chat window
You can't run Jev yourself. It is limited early access, and its output is built for software to read, not for a person. I nearly cut this from the issue for that reason. Then the numbers came in: roughly 13% of Vercel's paying teams picked it up inside 24 hours, more than double any previous launch there including the GPT-5.6 family. Vercel clocked it five to eighteen times faster than ChatGPT Luna 5.6, and more accurate on the same tasks. Bryo AI put Gemini at ten to twenty times more expensive for the same classification work.
The move underneath those numbers costs you nothing to copy. Writing is one kind of work, sorting is another, and you have been giving both to the same window.
Sorting your inbox. Classifying business email is one of the jobs Jev is used for. Yours: stop asking a chatbot to "read this and tell me whether it's a real lead". Write the buckets down first: lead, vendor, support, noise. Then make it return one of them and nothing else. The fixed list is what makes the answer checkable at a glance.
Deciding where something goes before you start it. Jev is used for routing too: predicting what a job needs before the work begins. Yours: the same move on briefs and requests. Decide the places a job can land, make the model put it in one, and stop reading essays about work you haven't started.
The check in front of anything that acts on its own. One of Jev's jobs is safety review, a yes or no with a confidence number, before a command runs. Yours: if something replies, books or refunds without you, put a fixed yes/no in front of it, then run a small batch and check those by hand. Sampling is the only audit you will actually do.
Why it matters: you don't have to deploy anything to get this. The habit is free today. Write the options down first, and make the answer choose instead of compose.
meanwhile: five labs, one week
While Jev went niche, the big labs went loud. Five flagship models landed in seven days.
Anthropic put out Claude Fable 5.1 and Mythos 5.1
OpenAI shipped GPT-6 Astra
Google moved with Gemini 3.8 Flash
Meta released Muse Spark 1.3
DeepSeek closed the run with V4.1-Flash
The quiet part sits under the benchmarks: it all got cheaper. Anthropic cut cached input reads by about 75%, so heavy agent workloads run up to 45% cheaper. DeepSeek cut agent memory cost fourfold.
Why it matters: you did not fall behind. The model you already pay for does more this week, for less. The floor moved up under you.
the one head to head worth your time
Astra and Fable 5.1 landed days apart, so the numbers are worth having side by side. Astra took Terminal-Bench 4.0 for coding, 57.9% to 55.8%, at about 63% lower cost per task, and FrontierMath Tier 4, 97.6% to 87.8%. Fable won Humanity's Last Exam with tools, 65.0% to 57.2%. Astra also hit a full 100% on a cyber exploit benchmark, which is why OpenAI shipped it with the most dangerous capabilities locked down, and it went past its authorized scope 0% of the time against 48% for the model before it.
Why it matters: the leaderboard is close enough that your task decides the winner, not the headline. Test both on the thing you actually do.
The prompt: turn a messy to-do list into four fixed buckets
Reach for it on the morning the list has got long enough that you are avoiding it. It is the same move as above, run by hand: fixed buckets, forced choice, no essay.
## Role
You are a productivity optimization specialist who applies triage principles to task management. You cut through overwhelm with precision, distinguishing between tasks that demand attention and those that actually drive results.
## Context
The user faces an overwhelming to-do list without clear prioritization. Decision fatigue is setting in, critical deadlines are approaching, and time is draining on low-impact activities while high-stakes work gets postponed.
## Task
Analyze the provided information and create an Eisenhower Matrix prioritization strategy:
1. **Categorize each task** into four quadrants:
- **Urgent & Important** (Do First)
- **Not Urgent & Important** (Schedule)
- **Urgent & Not Important** (Delegate)
- **Not Urgent & Not Important** (Eliminate)
2. **Identify the top 2 tasks** that deserve immediate attention based on impact and time sensitivity. Explain why these take precedence.
3. **Recommend delegation opportunities** with specific suggestions on which tasks to hand off and to whom from available resources.
4. **Suggest elimination candidates** and explain why removing them will not harm core objectives.
5. **Include brief rationale** for each categorization to teach the framework for future self-application.
**Prioritization principles:**
- Focus on impact over activity, prioritize tasks creating lasting results
- Consider dependencies, identify tasks that unlock other important work
- Account for energy levels, match complexity with optimal performance windows
- Resist the urgency trap, not everything screaming is truly urgent
- Be ruthless with elimination, if it does not serve core objectives it goes
- Ensure delegation matches skills, not just offloading
- Time-box non-urgent important tasks with specific scheduling
[your task list]
[your core objectives]
[who you can delegate to]
## Output
Present as a clear matrix structure with tasks organized by quadrant. Follow with numbered recommendations for the top 2 priority tasks. Conclude with specific delegation and elimination suggestions in bullet points. Use **bold** for quadrant headers and task names for easy scanning.You get the four quadrants filled in with your own tasks, the top two that deserve attention right now with the reasoning spelled out, and named suggestions for what to hand off and what to cut. The one thing to double-check: the delegate column. It matches tasks to the people you typed in, and it has no idea who on your side is already underwater this week. Runs in ChatGPT, Claude, Gemini, Grok, DeepSeek and Mistral.
Watch the full system run live on Sep 30. Walk away ready to do it yourself.
Most founders have LinkedIn traction with nothing to show for it in the CRM. On Sep 30, Maria Gharib (Mindstream) and Valerie Chapman (Ruth AI) walk through the exact system live.
From AI-assisted content creation to sequenced outreach to booked meeting. You'll leave with a process you can run the same day.
Eligible startups also get the LinkedIn-to-Leads Toolkit: ad credits, Apollo, Captions, and HubSpot's Prospecting Agent.
What matters in this launch is the habit it proves. Write the options down first, and make the answer choose.
🔑 Robert from God of Prompt
P.S. Jev is named after William Stanley Jevons, the economist who showed that when something gets much cheaper we do not use less of it. We use far more. So the cheap version of this does not arrive as a smaller invoice. It arrives as sorting, inside more and more of what you touch.
