ExpertsFMJev is HERE. How to use it
The Startup Ideas Podcast · September 18, 2026
5 Selected Lessons:
heuristic · 2 framework · 1 case · 1 priority · 1
Match tool type to task type: decision models vs reasoning models
Source
The Startup Ideas Podcast — Jev is HERE. How to use it (September 18, 2026) · at 11:15 ▸ · at 14:12 ▸ · at 17:01 ▸ · at 22:59 ▸
When this applies
when selecting among multiple available AI or automation tools for a business problem
The takeaway
Rank tool choice by task complexity: use the fastest/cheapest tool capable of the task, and reserve higher-cost reasoning tools for tasks that genuinely require synthesis or high-stakes judgment.
Why it works
Classifiers that skip text generation and internal reasoning are dramatically faster and cheaper but lack the capacity to synthesize context or handle nuanced, high-stakes judgment that reasoning models provide.
One level up
Efficiency gains come from using the minimum viable intelligence for each sub-task, not applying the most powerful tool everywhere.
Where it stops holding
This stops working when task complexity is misjudged — applying a classifier to a task that actually needs reasoning (as shown in the Bitcoin case) produces poor results.
Detail
hierarchy:
- 1. High-speed, low-stakes, repetitive triage/classification tasks (email sorting, lead scoring, support routing) → use a fast classifier like Jev
- 2. Complex tasks requiring context synthesis, external information, or high stakes (trading decisions) → use a frontier reasoning model or human judgment
- 3. Pure conversational tasks → neither; classifiers like Jev are explicitly not designed for this
rationale: Mismatching tool complexity to task complexity either wastes money (overusing reasoning models for simple triage) or produces poor outcomes (underusing reasoning for complex prediction).
In their words
This isn't that at all. This is still AI because it's like machine learning, but it's a decision model strictly.
Schema-based probabilistic classification
Source
The Startup Ideas Podcast — Jev is HERE. How to use it (September 18, 2026) · at 2:23 ▸ · at 4:28 ▸ · at 6:53 ▸ · at 9:08 ▸ · at 11:15 ▸
When this applies
when you need to make a fast, repeatable classification decision across a high volume of unstructured inputs
The takeaway
Constraining an AI's output space to a fixed schema turns an open-ended reasoning task into a fast, cheap, type-safe decision task.
Why it works
Instead of generating conversational text, the model is given a defined output schema (categories/fields) and returns probability-weighted confidence scores for each option, which can be used directly in code without further parsing.
One level up
Match the shape of the tool to the shape of the problem: narrow, structured problems deserve narrow, structured tools, not general-purpose reasoning engines.
Where it stops holding
This stops working when the decision requires deep contextual reasoning, cross-referencing external information, or weighing many interacting factors that a fixed schema can't capture.
Detail
procedure:
- Define an input (e.g., an email object, a phone, a video transcript)
- Define an output schema with fixed categories/fields (e.g., category, priority, spam score)
- Pass input + schema to the classifier model
- Model returns probability-weighted scores for each schema option
- Use the structured, type-safe output directly in code/automation
benchmarks: {"cost":"18 cents for 1,700 emails with 4.2M input tokens and 500K output tokens","latency":"around 200 milliseconds per query regardless of input/output structure"}
rationale: Removing free-text generation and reasoning steps makes the model orders of magnitude faster and cheaper while still producing usable, structured decisions.
In their words
It just basically gives you this object, which is the structure of the data.
Add a classifier at the decision layer
Source
The Startup Ideas Podcast — Jev is HERE. How to use it (September 18, 2026) · at 17:01 ▸ · at 20:48 ▸
When this applies
when any part of your workflow forces you to look at data and make a quick judgment call
The takeaway
Identify every point in a process where a human currently pauses to make a quick judgment, and consider automating that specific decision point rather than the whole process.
Why it works
Fast, cheap classification can replace or pre-filter the moment a human or system would otherwise have to manually evaluate and route information.
One level up
Automation is most valuable not at the edges of a workflow but at its decision bottlenecks.
Where it stops holding
This stops working when the decision requires holistic judgment, relationship context, or stakes too high to delegate to a probability score without human review.
Detail
rule: If you're looking at some data and thinking 'I have to make a decision on this,' consider adding a classifier at that layer.
threshold: Not for 100% of interactions — reserve high-stakes or advisory decisions for humans or more capable reasoning models.
rationale: Classifiers excel at split-second, repetitive triage decisions (lead quality, support routing) but shouldn't replace judgment-heavy interactions.
In their words
If you're looking at some data and thinking, Hmm, I have to make a decision on this. You should probably think about adding Jev at that layer.
Cheap trial before commitment
Source
The Startup Ideas Podcast — Jev is HERE. How to use it (September 18, 2026) · at 14:12 ▸ · at 22:59 ▸
When this applies
when evaluating whether to adopt any new low-cost tool or platform for your business
The takeaway
When marginal cost per use is near zero, the right strategy is to experiment heavily and let usage reveal ideas, rather than plan extensively upfront.
Why it works
Because per-use cost is extremely low, a small deposit can sustain extensive experimentation, lowering the risk barrier to discovering valuable use cases.
One level up
Low cost of experimentation should be exploited through volume of trials, not caution.
Where it stops holding
This stops working when the tool's cost structure isn't actually this cheap at scale, or when experimentation itself carries non-monetary risk (e.g., data privacy, reputational).
Detail
rule: Load a small amount of credit ($10) and experiment broadly; the cost is so low you likely won't notice it.
threshold: $5 credit lasted the OpenCode team two days of heavy demo use; $10 could last roughly three months.
rationale: Near-zero marginal cost removes the usual financial risk of experimenting with a new AI tool, so the bottleneck becomes creativity, not budget.
In their words
We had like a $5 like, I guess, like intro credit, I guess, on the account. We were able to use that for two days without hitting it.
1,700 emails classified for 18 cents
Source
When this applies
when you have a large backlog of repetitive, structured decisions that are too tedious or costly to do manually or with a full LLM
The takeaway
Bulk, repetitive classification tasks become economically trivial once you strip away unnecessary generative overhead.
Why it works
A lightweight classifier evaluates many structured fields per item at near-zero marginal cost because it skips free-text generation and reasoning overhead.
Where it stops holding
This stops working when the categorization logic needs nuanced judgment calls or reasoning that a fixed schema and simple probability scores can't capture.
Detail
situation: Ryan had 1,700 emails he wanted categorized by category, priority, spam score, and reply likelihood, a task that would normally take hours with manual review or a slow/expensive LLM.
decision: He ran the entire batch through Jev using a defined schema for all four outputs instead of processing each manually or via a conversational LLM.
outcome: All 1,700 emails were classified with accurate-seeming scores (e.g., 90% reply-worthy account violation flag) for a total cost of 18 cents and apparently rapid turnaround, versus an expected ~10 hours and high cost.
lesson: When a decision task is repetitive and well-defined, a narrow classifier can make bulk processing essentially free and instant, unlocking use cases previously seen as too costly to automate.
In their words
We had 4.2 million input tokens and 500,000 output tokens. The entire cost was 18 cents for each one of those emails, all categorized, all.