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teren de test TypeSafe: docs offline + script de proba

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Separat de produsele ROA. docs/ = documentatia oficiala descarcata ca Markdown
(111 pagini), reluabila cu update_docs.sh. typesafe_test.py face un apel cu cate
o intrebare din fiecare tip (choice/noul/score) pe o linie de factura de furnizor.
Cheia API se ia din TYPESAFE_API_KEY, nu se versioneaza.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KHLUSsKP99G6ebv2fFUKQV
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> ## Documentation Index
> Fetch the complete documentation index at: https://docs.typesafe.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# AI primer
> Why TypeSafe trains decision models with calibrated probabilities instead of optimizing for generated text.
Most AI products are built around a conversation between a model and a person. TypeSafe starts from a different bet: large-scale automation will be dominated by AI-to-AI and AI-to-software interactions, so the machine interface matters more than the chat interface.
> **We call this Machine Native Intelligence:**
>
> AI with software-like properties such as structure, reliability, observability, testability, speed, consistency, and low cost.
## Building prod, not God
TypeSafe is not trying to build a model that does everything. It is designed for production systems where code needs a narrow decision it can inspect and act on.
Our expectation is that large-scale AI automation will be closer to 99% machine-to-machine interactions and 1% human interaction. That shifts the design target from responses that feel good to read toward outputs that behave predictably inside software.
Read the [TypeSafe manifesto](https://typesafe.ai/manifesto).
## Three post-training approaches
Pretrained language models have been adapted in two major ways. TypeSafe adds a third. RLHF and RLVR are shown here for context; TypeSafe's training path is RLCD.
<Columns cols={3}>
<Card title="RLHF" icon="messages-square" type="note">
**Reinforcement learning from human feedback** turned pretrained models into chatbots. It trains models to produce responses people prefer.
</Card>
<Card title="RLVR" icon="brain-circuit" type="note">
**Reinforcement learning with verifiable rewards** created reasoning models that are strong at tasks such as mathematics, but slower and more expensive.
</Card>
<Card title="RLCD" icon="binary" type="tip">
**Reinforcement learning for calibrated decisions** trains TypeSafe to return decisions and calibrated probabilities instead of generated text.
</Card>
</Columns>
RLHF was used to train InstructGPT and ChatGPT and was [co-invented by Diogo Almeida](https://scholar.google.com/citations?user=0T4y07QAAAAJ\&hl=en), cofounder of TypeSafe.
<Frame>
<img className="block dark:hidden" src="https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-primer/training-paths-light.webp?fit=max&auto=format&n=aFVnpmCIX68NpsV1&q=85&s=61898215ac31388d3be15bf583b743ee" alt="Pretrained language models branch into muted RLHF and RLVR paths and an emphasized RLCD decision-model path." width="2048" height="810" data-path="images/ai-primer/training-paths-light.webp" />
<img className="hidden dark:block" src="https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-primer/training-paths-dark.webp?fit=max&auto=format&n=aFVnpmCIX68NpsV1&q=85&s=2747633edb0e54fa3f14a8aba830f4fd" alt="Pretrained language models branch into muted RLHF and RLVR paths and an emphasized RLCD decision-model path." width="2048" height="810" data-path="images/ai-primer/training-paths-dark.webp" />
</Frame>
## RLCD and calibrated decisions
RLCD optimizes for a different output contract:
* The model does not generate text.
* It returns decisions and probabilities.
* Higher probability should correspond to a greater chance that the answer is correct.
Calibration makes uncertainty usable by software. Across many predictions from a well-calibrated model:
* Outcomes assigned a probability of `0.2` should occur about 20% of the time.
* Outcomes assigned a probability of `0.8` should occur about 80% of the time.
* Outcomes assigned a probability of `1.0` should occur 100% of the time.
These rates describe groups of predictions, not a guarantee about any single answer. See [Confidence](/confidence) for guidance on deciding when software should act or escalate.
## The problems with RLHF
RLHF teaches a model to say things that people prefer. That objective works well for chatbots, but it can also reward sycophancy and confident-sounding hallucinations.
Preference optimization also causes **mode dropping**: the model learns to favor a particular style, such as instruction following, while reducing the probability of other possible outputs.
<Frame>
<img className="block dark:hidden" src="https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-primer/mode-dropping-light.webp?fit=max&auto=format&n=aFVnpmCIX68NpsV1&q=85&s=d51758a6212b526fc243cc9a81572cc7" alt="The probability distribution of a base model compared with a narrowed, mode-dropped distribution after RLHF." width="2048" height="1117" data-path="images/ai-primer/mode-dropping-light.webp" />
<img className="hidden dark:block" src="https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-primer/mode-dropping-dark.webp?fit=max&auto=format&n=aFVnpmCIX68NpsV1&q=85&s=4330e6251ca335515a61f61794f21389" alt="The probability distribution of a base model compared with a narrowed, mode-dropped distribution after RLHF." width="2048" height="1117" data-path="images/ai-primer/mode-dropping-dark.webp" />
</Frame>
<Warning>
An output can be compelling to a person without being reliable enough for unattended automation. Human preference and machine trustworthiness are different optimization targets.
</Warning>
Mode dropping is a milder version of **mode collapse**. In the classic generative-adversarial-network failure mode, a generator learns to produce the same kind of output repeatedly because that output continues to fool the discriminator.
<Accordion title="Mode collapse analogy">
<Frame>
<img className="block dark:hidden" src="https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-primer/mode-collapse-light.webp?fit=max&auto=format&n=aFVnpmCIX68NpsV1&q=85&s=2896f125ad1a5835b31b088fbc64eff1" alt="Repeated characters illustrate a GAN suffering from mode collapse." width="1084" height="759" data-path="images/ai-primer/mode-collapse-light.webp" />
<img className="hidden dark:block" src="https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-primer/mode-collapse-dark.webp?fit=max&auto=format&n=aFVnpmCIX68NpsV1&q=85&s=95645bdefd0bb3fa093edc3dd9308337" alt="Repeated characters illustrate a GAN suffering from mode collapse." width="1084" height="759" data-path="images/ai-primer/mode-collapse-dark.webp" />
</Frame>
</Accordion>
RLHF remains a good fit for conversational models. TypeSafe's position is that production automation needs a different training objective—one centered on constrained decisions and calibrated uncertainty.