苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养

✈Telegram/电报全球各个城市导航： (请注意用户名字母，其他都是骗子)
8年实力靠谱经纪人，全程通过人工甄核，资料验证与多重风险控制机制，确保平台模特资源真实、合规、可控、隐私、安全
互粉💯 💯 💯💯 💯 💯

[OSINTdefender @sentdefender]

According to Reuters, citing the British maritime security group Ambrey, the Palau-flagged oil tanker that suffered an explosion this morning causing an engine room fire off the coast of Oman, was likely the result of a U.S. missile strike as part of continued blockade operations against Iran.

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

Dreaming of waking up to optimism and adventure.

[Art Gallery @X_ArtGallery]

Alexandre Cabanel 1847

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

That first bite of warm homemade honey cake Pure weekday magic.

[🫱🏽选妃🚳点我🟥档次 @AeorPulse49688]

Spent 3 inspiring afternoons as a food bank volunteer packing meals for local seniors. Seeing their bright smiles when picking up the parcels made every minute worth it. Small acts, big impact!，

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

Autumn wind just arrived, the maple leaves are turning red, and the air smells like sweet osmanthus. The perfect small delight for a crisp weekend!，

[🤲🏽选妃🚹点我🔲气质 @UEangine82138]

Adjust your sleep schedule now—sleeping before 11 PM u0026 waking at 7 AM boosts energy, focus, and mood daily! Give it a try for 1 week and feel the difference.，

[Darlene @Darlene13989542]

A quick check-in with a friend who had a tough week made my whole day—small talks = big warmth

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

Keep your pothos thriving by placing it in bright indirect light and watering only when the top 2 inches of soil feel dry—easy!，

[Jennifer LaClare @JenniferLaClare]

A stranger helped me carry my heavy groceries up 5 flights of stairs when my hands were full. Grateful for this small warm surprise today

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

A stranger noticed my flat tire on my way to work u0026 pulled over to help fix it—no payment, just said "pass it on". Restored my faith in small kindnesses today.

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

Just vibing to a lo-fi beats playlist while rain taps the window—this is the calm I’ve been craving all week ，

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

This weekend, I volunteered at the community food drive—packed 200 care kits for local families in need. Nothing beats the warmth of helping neighbors!

[Evgenya Lepaeva @Evgenya_Lepaeva]

Quick u0026 tasty fat-burner lunch: grilled chicken breast + quinoa + roasted broccoli. High protein, low carb, ready in 15 mins!

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

中秋佳节限定特餐上线啦！芋泥流心广式月饼配桂花蜜烤桂花鸭，一口甜香裹着馥郁，限定100份，手慢无～

[重庆兼职/香港兼职/澳门兼职/武汉兼职/苏州兼职/南京兼职/合肥兼职/宁波兼职/昆明兼职/福州兼职 @songli1971]

蹲到写字楼最后一盏灯，奶茶全凉了但进度条拉满，今日份加班成就get，

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

Striving for a peaceful existence bathed in fulfillment.

[Commentary Donald J. Trump Posts From Truth Social @TrumpDailyPosts]

🚨TRUMP JUST DID IT ON SAVE AMERICA ACT!

He added the SAVE AMERICA Act to the Pentagon bill which only needs a simple majority 50 votes + JD Vance to pass!🔥

Trump is demanding GOP pass it immediately.

No more games!

HT @EricLDaugh

[苏州包养长沙包养郑州包养合肥包养青岛包养东莞包养宁波包养佛山包养成都包养泉州包养兰州包养湖州包养 @notte76]

Yearning for tranquility and hope every day.

[Benjamin Foullen @Benemporio]

LANCE MARTIN FROM ANTHROPIC SHOWED HOW TO DESIGN LOOPS WITH CLAUDE FABLE 5

Fable 5 is built differently from earlier models.
The old approach was prompt engineering: steer the model
with detailed instructions until it produces what you want.

Fable 5 rewards a different approach.
Design a loop with feedback, let the model self-correct,
and step back. Lance Martin from Anthropic ran experiments
that show exactly how much this matters.

—

TIP 1: SELF-CORRECTION LOOPS

The recipe: give the model a goal or rubric,
let it run, collect feedback, self-correct, repeat
until the goal is satisfied.

Lance tested this on Parameter Golf, an open-source
ML challenge: train the best model that fits in 16MB
in under 10 minutes on 8xH100s. The agent edits training
code, launches runs, reads the score, decides what to try next.

Fable 5 vs Opus 4.7 on the same challenge, 8 hours each:

Fable 5 improved the training pipeline ~6x more than Opus 4.7.

The difference in behavior is the interesting part.
Fable 5 bet on large structural changes (architecture edits)
and pushed through a quantization regression to reach
its biggest win. Opus 4.7 found a small win early and
repeated the same template: adjust a constant, measure,
keep if positive.

The takeaway: Fable 5 explores. It takes bigger swings
and recovers from setbacks instead of playing safe.

—

THE VERIFIER INSIGHT

One subtle point that changes results:
who does the judging matters.

Models struggle with self-critique on their own outputs.
A verifier sub-agent outperforms self-critique with Fable 5
because grading happens in an independent context window.

The agent doing the work and the agent grading the work
should not share context. Separate the roles.

—

TIP 2: MEMORY AS AN OUTER LOOP

Memory is a loop that spans across sessions.
The model writes to memory during one session,
retrieves it in future sessions.

Lance tested this on Continual Learning Bench 1.0:
an agent answers sequential SQL questions across separate
sessions, with shared memory between them.

The progression that effective memory requires:
fail → investigate → verify → distill → consult.

Where each model stops:

Sonnet 4.6 exits at step 1.
Its memory is a list of failure notes and guesses.
It rarely consults prior notes.

Opus 4.7 exits around step 3.
It creates a schema reference with uncertainty flagged,
but verification coverage is low (median run ~17%).

Fable 5 completes the progression.
In its strongest runs, verification coverage hit 73%
(22 of 30 questions). It distills learnings into general
rules that help future tasks.

The takeaway: Fable 5 doesn't just store failures.
It investigates them, verifies the fix, turns it into
a rule, and consults that rule instead of re-deriving it.

—

THE CORE LESSON

Rather than prompting and steering Fable 5 directly,
design loops that let it self-correct against environment
feedback and manage its own context through memory.

Two primitives to start:
→ a goal or rubric for self-correction
→ a shared memory store for cross-session learning

And one rule: use a separate verifier.
Don't let the model grade its own work in the same context.

Test it yourself on a hard task.
The longer and more complex the task,
the bigger Fable 5's lead.

—

source: Lance Martin (@RLanceMartin), Anthropic.
experiments: Parameter Golf, Continual Learning Bench 1.0.

[New York Post @nypost]

CBS News boss Bari Weiss poised to oversee CNN editorial operations: report https://t.co/9TOb1jnX68