Feels like an end of era, ordinary people will probably never again get upgraded frontier models.

Fable 5’s return shows how safety routing can downgrade a frontier model.

Now we only permissioned intelligence.

The cost of putting a gatekeeper inside intelligence.

To note, that safeguard is not a simple refusal layer; it is a classifier that sends flagged Fable 5 requests to Opus 4.8.

Fable 5 came back, but the old promise did not.

End of an era. ☹️

Anthropic is cooked the moment GPT-5.6 Sol becomes broadly available.

If OpenAI can offer similar or better intelligence at dramatically lower cost, then the competition shifts away from a pure model race and becomes an economics race.

In that context, OpenAI is clearly attacking inference cost hard now, reinforcing its strategic advantage.

As a result, you can’t compete with “smarter” if the other guy is both smarter and cheaper.

Nobody cares about your truly opinion until your financial status is level above them

June '26 LLM list (final)

Claude Fable 5 ✅ 🥇🐐
GLM 5.2 ✅ 🥇best open source
LFM2.5-230M ✅ Small CPU 🥇
Claude Mythos ✅ ...but not for you
Claude Sonnet-5 ✅
GPT-5.6 Sol ❌
Gemini 3.5 Pro ❌
Gemma 4 12B ✅
Grok 5 ❌
Grok 4.5 ❌
MiniMax M3 ✅ (weights released)
Nemotoron 3 Ultra ✅
Qwen3.7-Plus ✅
Qwen3.7-30B ❌ 🙏
Kimi K3.0 ❌
Kimi K2.7 Code✅
Nex-N2-Pro ✅
Hunyuan3 MoE ❌
Macron V1 Preview ✅
MAI Thinking-1 ✅
MAI Code-1-Falsh (coding model) → VSCode ✅
MAI Image-2.5 ✅
MAI Image-2.5-Flash ✅
MAI Transcribe-1.5 ✅
MAI Voice-2 ✅
MAI Voice-2-Flash ✅
Agents-A1 ✅
LongCat-2.0 (OwlAlpha) ✅
Ornith-1.0-35B ✅
Kog Laneformer 2B ✅
Sakana Fugu ✅
VibeThinker-3B ✅

The most expensive parts in humanoid robots are not the cameras.

The real hardware cost is hidden in the parts that move, carry load, survive impact and repeat the same motion thousands of times without failing.

Typical hardware cost range per humanoid robot:

• Dexterous hands
➝ $9K–$90K
The hardest part to make cheap. Each hand needs small actuators, tendons or linkages, tactile sensing, finger joints, wiring and control boards packed into a very small space.

• CNC metal frame
➝ $2K–$20K
The skeleton must hold motors, batteries, electronics and impact loads. Low volume machining makes this expensive, especially for torso, hip, shoulder and leg structures.

• LiDAR
➝ $1K–$15K
Used for mapping, navigation and obstacle detection. Cost depends on range, resolution, scan type and whether the robot needs outdoor reliability.

• Force-torque sensors
➝ $1K–$5K
Critical for balance, manipulation and safe contact. These sensors help the robot measure pressure through wrists, ankles or joints.

• Tactile sensors
➝ $500–$5K
Needed when a robot must grip soft, fragile or uneven objects. The cost rises fast when sensors cover fingers, palms or large skin-like surfaces.

• Actuator modules
➝ $300–$3K each
One of the biggest cost drivers. A humanoid can use dozens of actuators across legs, arms, waist, neck and hands. Torque, cooling, gearbox quality and control electronics change the price fast.

• Battery pack
➝ $500–$1.5K
The battery must deliver high current while staying compact. Weight is a major constraint because every extra kilogram makes the legs work harder.

• Compute / GPU
➝ $250–$2K
The robot needs onboard compute for vision, control, planning and sensor fusion. Higher autonomy requires more compute, better thermal design and more power.

• Harmonic drives
➝ $200–$2K each
Used where compact high torque is needed. They are expensive because precision, backlash and durability matter in knees, hips, shoulders and wrists.

• Power electronics
➝ $500–$5K
Motor drivers, converters, protection circuits and power distribution decide how stable the robot is under heavy motion.

• Wiring harness
➝ $300–$3K
Humanoids have cables moving through arms, legs, torso and neck. Bad routing means broken wires, noisy signals and hard maintenance.

• Precision encoders
➝ $50–$500 each
Every joint needs position feedback. Better encoders give smoother motion, better balance and more accurate manipulation.

A serious humanoid robot can still carry $35K–$180K+ in hardware before software, assembly, testing, repair stock, certification and support.

That is why the cheapest demo robot is not always the cheapest robot to deploy.

$IBM $IONQ $QBTS $QUBT $RGTI | The NSA and Army Research Office have launched QuantumEAGLe, a joint defense initiative to accelerate the U.S. quantum computing ecosystem.

The strategic framework scales the domestic manufacturing base for specialized hardware components.

$UMAC $RCAT $AVAV $KTOS $LUNR | German drone maker Quantum Systems secures a landmark $1.2B Series D round led by Blackstone and Airbus.

The capital wave drives an $8B valuation to scale multi domain autonomous military hardware and software.

Unemployment could hit 20% by 2028.

This would be the worst job market since the 1929 Great Depression.

Labor force participation has completely collapsed.

This is the warning sign right before unemployment surges.

President Trump says AI could be “much bigger than the internet” arguing that whoever leads the race will define the next era.

He added that the U.S. is currently “substantially No. 1” in AI.

President Trump says he will be ringing the stock market opening bells in the Oval Office on Tuesday.

This event is set to launch the "Trump Accounts" which are tax-advantaged investment accounts for children under 18, created under the One Big Beautiful Bill Act.

The U.S. Treasury deposits $1,000 for qualifying newborns, and families can add up to $5,000 annually. Funds generally remain locked in U.S. index funds until the child turns 18

Anthropic LEAKS the optimal prompting structure for Fable 5.

This is how you save tokens while getting elite outputs

The humanoid robot race will be decided inside the supply chain.

A robot body looks simple from the outside.

Inside every movement depends on a stack of expensive fragile and hard-to-scale components.

• Actuator modules
➝ Convert software commands into controlled movement
➝ Drive shoulders, hips, knees, elbows, ankles and wrists
➝ Bad actuators mean weak motion, heat issues and short service life

• Dexterous hands
➝ Decide what the robot can actually do
➝ Picking tools, opening doors, carrying objects and handling small parts all depend on the hand
➝ A humanoid without useful hands is mostly a walking demo

• Harmonic drives
➝ Give precision inside the joint
➝ Reduce backlash
➝ Help the robot move smoothly under load

• Battery packs
➝ Set runtime, weight and deployment time
➝ Every extra kilogram affects balance walking efficiency and payload
➝ Long runtime is one of the hardest problems for real work

• Compute / GPU
➝ Runs onboard AI, vision, planning and safety logic
➝ The robot has to react inside the body, not only in the cloud
➝ Latency matters when the robot is walking near people

• Cameras & sensors
➝ Give vision depth balance and force feedback
➝ The robot needs to see the floor, detect objects and understand contact
➝ No perception means no useful autonomy

• Torque motors
➝ Put power inside every joint
➝ Compact motors are critical for hips, knees, shoulders and wrists
➝ More torque with less heat means longer useful operation

• AI data & training
➝ Turns teleoperation, simulation and real failures into better behavior
➝ Walking, grasping and tool use need massive task data
➝ The robot learns from the stack behind it

The market will reward the companies that can control the full body stack:

• joints
• hands
• sensors
• batteries
• compute
• training data
• manufacturing
• repair network

I found the cheat code for using Fable 5 without going broke.

Everything you need to know about cutting Fable token spend

The robotics sector is about to completely take off.

Robotics is tracing the exact same growth curve as software AI, just a few years behind.

We are likely entering the beginning of exponential growth for this ecosystem.

At current capabilities, robotics intelligence is at the level of roughly GPT-2.

(Capable, but lacking real-world field deployment)

Things are about to change.

Manufacturing costs and the general robotics cost curve is rapidly declining (Issac is only $8000!).

So bullish on the future of this sector.

Africa isn't scattered geology.... It's the most strategically concentrated mineral map on Earth.

🇨🇩 DRC: cobalt + copper giant

🇿🇲 Zambia: copper belt

🇿🇼 Zimbabwe: lithium surge

🇳🇦 Namibia: uranium + lithium

🇿🇦 South Africa: PGMs + manganese

🇲🇦 Morocco: phosphates

🇲🇱🇧🇫🇬🇭 Mali, Burkina Faso, Ghana: gold belts

🇬🇳 Guinea: bauxite

🇸🇱 Sierra Leone: emerging iron ore

Energy transition metals. Battery inputs.
Defense-critical minerals.
Concentrated on one continent, not scattered across it.
Africa isn't peripheral to the transition it's central to it.

Stop treating Fable 5 like every other AI model.

There are six things you need to know if you want to get the most out of Fable (save this):

1. Run Time

Every Claude model before Fable 5 worked in relatively short bursts.
Claude Fable 5 is meant to sustain output over extended periods to complete multi-day, goal-directed tasks.

This is one of the biggest shifts. Fable 5 is meant for fully autonomous work (paired with /goal or /loop).

2. It Gets Things Right First Time

One of the most reported early observations from people using Fable 5 is how rarely they need to iterate. Early testers reported single-pass implementations of systems that previously took days of iteration.

3. Clarifying Questions

To execute autonomous work loops accurately, Fable 5 may ask a series of clarifying questions before it kicks off its autonomous run.

4. Agent Management

Fable 5 is built to manage multiple parallel subagents at once (spinning up 50+ agents for complex tasks).

5. It "Sees" Better

Claude Fable 5 interprets dense technical images, web applications, and detailed screenshots with substantially higher accuracy.

For anyone using Claude to analyse charts, screenshots, documents, or visual data, the improvement here is meaningful.

6. Coding + Security Audits

It's no secret that Fable is a coding genius. This new model is especially powerful for codebase review and debugging.

$AXTI just finalized a massive 3-year Master Development and Supply Agreement with tech giant $COHR.

To secure production capacity, $COHR is giving $AXTI a $22.28 million upfront cash payment.

This is non-dilutive liquidity that will directly fund the aggressive expansion of $AXTI facility through 2028.

When a company like $COHR pays upfront to lock you in, it is the ultimate validation of your technology.

The agreement focuses on the mass production of 6-inch Indium Phosphide (InP) wafer substrates. InP is the foundational material required for laser components inside next-generation 1.6-Terabit optical transceivers.

As AI clusters grow exponentially, these optical transceivers are non-negotiable to prevent network bottlenecks. $AXTI is sitting right at the bottleneck of the global AI buildout.

Due to strict regulatory export restrictions, the market for high-end wafers is facing severe supply shortages, which previously drove prices up by up to 250%.

By securing $COHR as a primary partner, $AXTI guarantees a massive, high-margin revenue baseline for the next three years, effectively protecting its business model while riding a hyper-growth wave.

Microsoft CEO Satya Nadella's new interivew: Explains how the next AI moat will not be the model you use, but the learning loop only your company can run.

He is really asking what happens to the firm when intelligence becomes something you can rent.

For a century, companies protected value through people, processes, data, routines, customer memory, and the tacit knowledge buried in daily operations.

Foundation models threaten to flatten that advantage because the same general intelligence can be used by everyone.

Nadella’s answer is that firms need their own “hill climbing machine,” a private loop where models learn from company-specific tasks, traces, evaluations, and outcomes.

That means the real asset is not just the model.

The asset is the environment that keeps improving the model in ways competitors cannot copy.

Private evals become strategic memory.

Workflow traces become training signal.

Human judgment becomes a way to steer compounding, not just correct mistakes.

This also reframes AI adoption: a company that only consumes a foundation model may gain productivity, but it may leak the deeper value of its operating knowledge.

A company that builds a disciplined learning loop can turn everyday work into accumulating IP.

The future firm may therefore be measured by how well it converts its unique activity into durable model improvement.

The frontier will not belong only to whoever owns the largest model.

It will belong to whoever owns the best loop.