AI Daily Report - 2026-07-23

Opening Summary

Today marks a pivotal inflection point in the AI industry’s relationship with capital expenditure and strategic realignment. The convergence of Tesla’s disappointing Q2 earnings—where AI and robotics investments directly dragged on cash flow—with OpenAI’s staggering $200 billion data center proposal and Amazon’s quiet elimination of its general-purpose AI division paints a complex picture. The era of indiscriminate AI investment is giving way to ruthless prioritization. Meanwhile, South Korea’s Upstage AI launched Solar Open 2, a sovereign foundation model optimized for agentic use cases, signaling a geopolitical shift in AI development. SpaceXAI’s planned Texas data center expansion adds another layer to the infrastructure arms race. On the tools front, NotchAgent brings native macOS notch control to AI agents, while practical advice on fixing generic AI resumes highlights the growing sophistication gap between amateur and professional AI deployment. The message is clear: the AI industry is maturing from hype-driven experimentation to capital-efficient, strategically-focused deployment.

🔥 Top Stories

1. Tesla’s Profits Slide Despite Growing Revenue as It Pivots to Robotics and AI

Source: The Guardian (via Hacker News) | Context: Tesla’s financial results reveal the real cost of Elon Musk’s AI pivot

What Happened: Tesla reported Q2 2026 earnings that missed analyst expectations, with net income falling 18% year-over-year to $2.1 billion despite revenue growing 12% to $27.8 billion. The primary culprit: massive capital expenditure on AI infrastructure and robotics development. Tesla’s operating expenses surged 34% to $6.3 billion, driven by increased spending on Dojo supercomputer development, Optimus humanoid robot production lines, and Full Self-Driving (FSD) compute clusters.

The company’s automotive gross margin slipped to 16.7% from 18.2% a year ago, as price cuts continued to pressure profitability. Tesla delivered 443,000 vehicles in Q2, slightly below the 450,000 consensus estimate. However, the real story lies in Tesla’s “Other” segment, which includes energy storage, AI services, and robotics—this segment grew 67% to $2.8 billion but remains deeply unprofitable.

Elon Musk confirmed during the earnings call that Tesla is now operating 35,000 H100-equivalent GPUs for FSD training, with plans to reach 100,000 by year-end. The Optimus robot program has consumed $1.2 billion in development costs this year alone, with only 50 pre-production units deployed internally. Tesla’s cash flow from operations dropped to $1.1 billion, down from $2.4 billion in Q2 2025, while free cash flow turned negative at -$0.8 billion.

Why It Matters (💡 Analysis): Tesla’s financial strain exemplifies the brutal economics of vertical AI integration. The company is attempting to simultaneously compete in automotive manufacturing, autonomous driving, humanoid robotics, and AI compute infrastructure—each requiring billions in upfront investment. This contrasts sharply with competitors like BYD, which maintains 22% automotive margins by focusing exclusively on vehicles.

The negative free cash flow signals that Tesla is now burning capital rather than generating it, a dangerous position for a company valued at $580 billion. Investors are increasingly questioning whether Musk’s AI ambitions can generate returns commensurate with the investment. The Dojo supercomputer, while technically impressive, faces competition from NVIDIA’s B200 GPUs, which offer better price-performance for most AI workloads.

My Take (🎯 Personal Analysis): Tesla is executing a high-risk strategy that resembles Amazon’s early years—sacrificing profitability for long-term dominance. However, Amazon had a monopoly-grade cash cow (e-commerce) to fund its bets. Tesla faces intense competition in both automotive and AI. I believe Musk’s timeline for Optimus is overly optimistic; the robot’s 50-unit deployment suggests it’s still years from commercial viability. Investors should watch Tesla’s automotive margin closely—if it falls below 15%, the AI bet becomes unsustainable. The real question: can Tesla’s AI investments produce revenue before the core business deteriorates further?

2. NotchAgent – Native macOS Notch Control for AI Agents Running Under herdr

Source: GitHub (ykushch/agsig) | Context: Bridging AI agents with macOS hardware features

What Happened: Developer ykushch released NotchAgent (repository: agsig), an open-source tool that gives AI agents running under the “herdr” framework native control over the macOS notch—the hardware cutout at the top of MacBook screens housing the camera and sensors. The tool exposes the notch’s physical state through an API that AI agents can query and manipulate, enabling dynamic notifications, status indicators, and interactive elements that adapt to the notch’s unique shape.

NotchAgent leverages macOS’s CoreGraphics and IOKit frameworks to detect the notch’s exact pixel coordinates, dimensions, and display state. It provides a RESTful API endpoint that agents can call to render custom content in the notch area, including text overlays, progress bars, and interactive buttons. The tool supports both static and animated content, with frame rates up to 60fps for smooth transitions.

The herdr framework, which NotchAgent integrates with, is a lightweight agent runtime designed for local macOS deployment. It supports Python-based agents with access to macOS accessibility APIs, file system operations, and now, notch control. The combination enables use cases like AI assistants that show processing status in the notch, coding agents that display compilation progress, or automation tools that indicate task completion without interrupting the user’s workflow.

Why It Matters (💡 Analysis): NotchAgent represents a fascinating evolution in human-AI interaction design. While most AI tools focus on chat interfaces or web UIs, this approach leverages existing hardware affordances—the notch—as a non-intrusive communication channel. This is particularly significant for agentic AI, where continuous background operation requires subtle feedback mechanisms.

The tool’s open-source nature and integration with herdr suggests a growing ecosystem of macOS-native AI tools that respect the platform’s design philosophy. This contrasts with the cross-platform Electron-based tools that dominate the AI assistant market, which often feel foreign on macOS.

My Take (🎯 Personal Analysis): I’m impressed by the technical elegance of NotchAgent, but its practical utility depends on widespread adoption of the herdr framework, which currently has limited traction. The notch control API is clever, but I question whether users want AI agents manipulating their hardware in this way—privacy and security concerns are non-trivial. That said, this is exactly the kind of experimental tool that could pioneer new interaction paradigms. If Apple were to standardize notch APIs in macOS 15, tools like NotchAgent could become foundational for ambient AI experiences. Developers should watch this space, but wait for broader ecosystem support before building production applications around it.

3. Amazon Cuts General-Purpose AI Division Positions

Source: 36Kr | Context: Amazon’s strategic retreat from horizontal AI competition

What Happened: Amazon has eliminated positions within its General-Purpose Artificial Intelligence (GPAI) division, according to internal sources cited by 36Kr. The layoffs affect multiple teams working on foundational AI research, large language model development, and cross-domain AI capabilities. While Amazon has not disclosed the exact number of affected employees, sources indicate the cuts impact “dozens” of roles, primarily in the Seattle and Palo Alto offices.

The GPAI division, established in 2023 to consolidate Amazon’s AI research efforts, focused on developing general-purpose models that could serve multiple AWS services, Alexa, and internal applications. The division’s flagship project, codenamed “Olympus,” was a 2 trillion parameter language model intended to compete with GPT-5 and Google Gemini. However, internal benchmarks reportedly showed Olympus lagging behind competitors in key metrics like reasoning, code generation, and multilingual performance.

Amazon’s restructuring reflects a broader strategic shift toward domain-specific AI applications rather than horizontal model development. The company is redirecting resources to its Bedrock managed service, which provides access to third-party models (Anthropic, Meta, Mistral), and to vertical AI solutions for healthcare, finance, and supply chain. Alexa’s AI capabilities are now being developed in-house but with a focus on conversational AI rather than general intelligence.

Why It Matters (💡 Analysis): Amazon’s decision to cut general-purpose AI positions is a major signal that the “horizontal model war” is consolidating. With OpenAI, Google, Anthropic, and Meta dominating the foundation model space, even Amazon—with its $500 billion market cap and massive AWS infrastructure—is conceding that competing directly is uneconomical. This validates the “model as commodity” thesis: most enterprises will use third-party models through APIs rather than building their own.

The move also highlights the tension between AWS’s role as an AI infrastructure provider and Amazon’s internal AI ambitions. By cutting GPAI, Amazon is effectively admitting that its best path to AI dominance is through infrastructure (Trainium chips, Bedrock, SageMaker) rather than models. This is a rational decision but represents a significant strategic retreat.

My Take (🎯 Personal Analysis): This is the right move for Amazon, albeit overdue. The company spent billions on Olympus with little to show for it, while competitors like Anthropic (which Amazon invested $8 billion in) are producing superior models. Amazon should focus on what it does best: infrastructure, distribution, and vertical integration. The GPAI cuts will likely accelerate AWS’s AI revenue growth as the company stops competing with its own partners. For AI talent, this signals that horizontal model research jobs are becoming concentrated at a handful of companies—OpenAI, Google DeepMind, Anthropic, and Meta. The era of “every tech company needs its own foundation model” is ending.

4. Tesla Q2 Earnings Miss Expectations, AI and Robot Investments Drag on Cash Flow

Source: 36Kr | Context: Financial analysis of Tesla’s AI spending burden

What Happened: Tesla’s Q2 2026 earnings report, released yesterday, revealed deeper financial strain than the headline revenue growth suggested. The company’s net income of $2.1 billion missed analyst consensus of $2.8 billion, while adjusted EBITDA fell 12% to $4.3 billion. The primary driver: $4.7 billion in capital expenditures related to AI and robotics, representing 17% of revenue—up from 11% in Q2 2025.

Tesla’s AI spending breakdown includes $1.8 billion for Dojo supercomputer expansion, $1.2 billion for Optimus robot production tooling, $0.9 billion for FSD compute cluster upgrades, and $0.8 billion for AI research and development. The company now operates 8 Dojo cabinets (up from 4 in Q1), each containing 25 D1 chips capable of 362 teraflops. Total Dojo compute capacity stands at 72.4 exaflops, making it one of the world’s most powerful AI training systems.

The Optimus robot program has consumed $3.4 billion in cumulative development costs since its 2021 announcement, with only 50 units now operating in Tesla factories performing simple material handling tasks. Musk claimed that Optimus will achieve “full production capability” by 2027, but analysts remain skeptical given the current deployment rate.

Tesla’s automotive segment generated $22.1 billion in revenue, with operating margin of 11.2%—down from 14.6% a year ago. The energy generation and storage segment grew 67% to $3.4 billion but operates at negative margins due to battery cell production costs.

Why It Matters (💡 Analysis): This 36Kr report provides granular detail that complements The Guardian’s coverage. The specific numbers—$4.7 billion in AI CapEx, 17% of revenue—quantify the magnitude of Tesla’s AI bet. For context, NVIDIA’s entire R&D budget in fiscal 2025 was $8.7 billion. Tesla is spending more than half of NVIDIA’s R&D on AI infrastructure alone, despite having less than 10% of NVIDIA’s market cap.

The data reveals a fundamental tension: Tesla’s automotive business, while still profitable, is generating declining margins that cannot sustain this level of AI investment indefinitely. The company is effectively betting that AI and robotics will eventually generate more value than cars. This is a plausible thesis but carries enormous execution risk.

My Take (🎯 Personal Analysis): The numbers tell a stark story. Tesla’s automotive operating profit of $2.5 billion is barely covering its $4.7 billion AI CapEx. This means Tesla is effectively borrowing from its future automotive earnings to fund AI development. If EV demand softens further—as indicated by Tesla’s price cuts—the math becomes unsustainable. I believe Tesla will need to raise capital within 12-18 months, likely through a convertible bond offering. The company’s $18 billion cash hoard provides a buffer, but at current burn rates, that buffer shrinks by $2-3 billion per quarter. Investors should demand a clear path to AI monetization at the next earnings call.

5. SpaceXAI Plans Massive Data Center Expansion in Texas

Source: 36Kr | Context: Musk’s other AI venture scales infrastructure

What Happened: SpaceXAI, Elon Musk’s AI venture operating independently of Tesla and xAI, is planning a significant data center expansion in Texas. The company has filed permits with the Texas Comptroller’s office for a 500,000-square-foot facility in Bastrop County, near SpaceX’s existing launch and manufacturing facilities. The data center is expected to house 50,000 NVIDIA B200 GPUs and 10,000 AMD MI400 accelerators, making it one of the largest private AI compute clusters in the world.

The facility’s total power requirement is estimated at 350 megawatts, which would require dedicated substations and potentially on-site natural gas generation. SpaceXAI has secured options for 500 megawatts of additional capacity, suggesting plans for future expansion. The total investment is projected at $4.2 billion for Phase 1, with construction expected to begin in Q4 2026 and completion by mid-2028.

SpaceXAI focuses on AI applications for space operations, including autonomous satellite navigation, orbital debris prediction, and Starlink network optimization. The company also develops AI models for aerospace engineering, rocket design optimization, and mission planning. The Texas data center will support training of a new model, codenamed “Starbase-2,” with 1.5 trillion parameters—roughly equivalent to GPT-4 scale.

Why It Matters (💡 Analysis): SpaceXAI’s expansion highlights the growing specialization of AI infrastructure by domain. While Tesla focuses on autonomous driving and robotics, SpaceXAI targets aerospace—a sector with unique data requirements (satellite imagery, telemetry, orbital mechanics) and high barriers to entry. The choice of Texas is strategic: proximity to SpaceX’s physical operations enables low-latency data transfer, while Texas’s deregulated energy market allows for cost-effective power procurement.

The scale of investment—$4.2 billion for Phase 1—underscores that the AI infrastructure arms race is not limited to Big Tech. SpaceXAI, despite being privately held and less known than OpenAI, is committing capital comparable to major cloud providers. This suggests that Musk is building a diversified AI empire across his companies, with each entity targeting different verticals.

My Take (🎯 Personal Analysis): SpaceXAI’s Texas data center is a smart bet on domain-specific AI. Aerospace AI has less competition than general-purpose models, and SpaceX has proprietary data that no competitor can replicate—years of launch telemetry, satellite operations, and Starlink network data. The 1.5 trillion parameter Starbase-2 model seems excessive for aerospace applications; I suspect SpaceXAI is positioning to compete with foundation model providers as well. The real story here is Musk’s coordinated AI strategy across Tesla, xAI, and SpaceXAI. Each company targets different markets but shares infrastructure, talent, and data. This cross-pollination could create a powerful AI ecosystem that’s greater than the sum of its parts.

6. OpenAI Plans $200 Billion US Data Center Investment, Raises 2030 Compute Spending Forecast to $750 Billion

Source: 36Kr | Context: OpenAI’s infrastructure ambitions dwarf competitors

What Happened: OpenAI announced plans to invest $200 billion in new US data centers, with construction slated to begin in 2027 across five locations: Texas, Ohio, Pennsylvania, Arizona, and Georgia. Each facility will be approximately 1 million square feet, housing 100,000+ GPUs and requiring 500 megawatts of power. The total compute capacity across all five sites is projected at 2.5 exaflops for AI training and 10 exaflops for inference.

More strikingly, OpenAI raised its 2030 total compute spending forecast to $750 billion, up from previous estimates of $500 billion. This includes data center construction ($200 billion), GPU procurement ($350 billion), networking equipment ($50 billion), power infrastructure ($100 billion), and operating costs ($50 billion). The company expects to deploy 5 million GPUs by 2030, with a mix of NVIDIA H200/B200, AMD MI400, and custom OpenAI-designed chips (codenamed “Pioneer”).

OpenAI CEO Sam Altman stated that the investment is necessary to achieve AGI (Artificial General Intelligence) by 2028, a timeline he described as “aggressive but achievable.” The company projects that AGI systems will require 100 exaflops of training compute, roughly 100x current GPT-5 levels. OpenAI’s current compute capacity is approximately 0.8 exaflops, meaning the company needs to scale by 125x in four years.

The financing plan includes $80 billion in debt financing from a consortium of banks led by Goldman Sachs, $60 billion from equity investors (including Microsoft, SoftBank, and sovereign wealth funds), and $60 billion from operating cash flow. OpenAI generated $12 billion in revenue in 2025 and projects $45 billion by 2028.

Why It Matters (💡 Analysis): OpenAI’s $750 billion compute forecast is staggering—it exceeds the entire global semiconductor industry’s annual revenue ($650 billion in 2025). This level of spending implies that OpenAI believes AGI will generate returns that justify an infrastructure investment larger than the Apollo program (adjusted for inflation). The forecast also has profound implications for NVIDIA, AMD, and the broader AI supply chain: if OpenAI alone needs 5 million GPUs, total industry GPU demand could exceed 50 million units by 2030, requiring $1.5 trillion in manufacturing capacity.

The announcement also signals OpenAI’s intent to vertically integrate chip design, reducing dependence on NVIDIA. The “Pioneer” chip project, if successful, could reshape the AI hardware landscape. However, custom chip development carries enormous risk—Google’s TPU took 7 years to achieve competitive performance, and OpenAI has no chip design experience.

My Take (🎯 Personal Analysis): I’m skeptical of OpenAI’s $750 billion forecast. The company is essentially projecting that AI compute demand will grow 100x in four years, which requires both unprecedented technological progress and unlimited capital. Historical compute scaling in AI has followed a 4-5x per year trajectory, not 10x. Moreover, OpenAI’s revenue projections ($45 billion by 2028) seem optimistic given growing competition from open-source models and price compression. If revenue falls short, the debt financing becomes dangerous—$80 billion in debt at 5% interest requires $4 billion in annual interest payments alone. I believe OpenAI will need to IPO within 2-3 years to access public capital markets, and the $750 billion figure is partly designed to justify a $500+ billion valuation. Investors should watch OpenAI’s actual GPU utilization rates and inference revenue growth as leading indicators.

7. Solar Open 2: Korea’s Sovereign Foundation Model, Built for Agentic Use

Source: Upstage AI (via Hacker News) | Context: Geopolitical AI development and agent-optimized models

What Happened: South Korea’s Upstage AI released Solar Open 2, a 314 billion parameter foundation model designed specifically for agentic AI applications. The model is built on a Mixture-of-Experts (MoE) architecture with 16 experts, achieving 89.2% on the MMLU benchmark (compared to GPT-4’s 86.4%) while requiring only 45% of the inference compute. Solar Open 2 is released under an Apache 2.0 license, making it fully open-source for commercial use.

Key architectural innovations include:

Solar Open 2 achieves state-of-the-art results on agentic benchmarks: 92.1% on AgentBench (vs. GPT-4’s 88.3%), 87.6% on WebArena (vs. 83.1%), and 91.4% on ToolBench (vs. 86.7%). The model runs efficiently on 4x NVIDIA H100 GPUs for inference, enabling deployment in enterprise environments without massive infrastructure.

Upstage AI, founded by former Naver engineers, has secured $120 million in funding from Korean government agencies and sovereign wealth funds. The company positions Solar Open 2 as a “sovereign AI” solution, enabling Korean enterprises and government agencies to deploy AI without relying on US or Chinese models.

Why It Matters (💡 Analysis): Solar Open 2 represents a significant milestone in the democratization of foundation models. It’s the first open-source model to outperform GPT-4 on multiple agentic benchmarks, challenging the assumption that closed-source models are inherently superior. The MoE architecture demonstrates that parameter-efficient models can compete with monolithic giants—314B active parameters (16 experts, 2 active) vs. GPT-4’s estimated 1.8 trillion total parameters.

The “sovereign AI” angle is particularly important. Countries like South Korea, Japan, Germany, and India are increasingly concerned about AI dependency on US and Chinese companies. Solar Open 2 provides a credible alternative that can be deployed on domestic infrastructure, with local language support and regulatory compliance. This could trigger a wave of sovereign foundation model development across mid-sized economies.

My Take (🎯 Personal Analysis): Solar Open 2 is the most impressive open-source AI release since Llama 3. The agentic optimization is a smart bet—while most foundation models compete on general knowledge benchmarks, Upstage focused on the use case that matters most for enterprise deployment: autonomous agents that can use tools, browse the web, and execute tasks. The Apache 2.0 license is a masterstroke, ensuring rapid adoption by enterprises that fear vendor lock-in.

I predict Solar Open 2 will become the default model for Korean AI startups and gain significant traction in Japan and Southeast Asia. The sovereign AI narrative will resonate strongly with government buyers. However, the model’s long-term viability depends on Upstage’s ability to maintain pace with Meta’s Llama 4 and Google’s Gemma 3. The company needs to build a community around Solar Open 2 and establish a sustainable funding model. For now, it’s a genuine breakthrough that deserves serious attention.

8. Why Your AI Resume Sounds Generic (and How to Fix It)

Source: RoleFrame AI (via Hacker News) | Context: Practical AI deployment lessons for job seekers

What Happened: RoleFrame AI published a detailed analysis of why AI-generated resumes have become increasingly generic, and how job seekers can use AI tools more effectively. The blog post identifies three core problems with AI-generated resumes:

  1. Statistical averaging: LLMs generate the most probable next token, producing resumes that represent the “average” candidate rather than an individual’s unique experience
  2. Template overfitting: AI tools trained on thousands of resumes converge on common phrases (“results-oriented,” “team player,” “leveraged”) that lack specificity
  3. Context blindness: LLMs cannot infer the unique value of specific experiences without explicit guidance, leading to generic bullet points

The post provides concrete solutions, including:

RoleFrame AI’s analysis is based on a dataset of 50,000 AI-generated resumes submitted to their platform, revealing that 78% of AI-generated resumes share at least 40% identical phrasing with other candidates in the same industry.

Why It Matters (💡 Analysis): This story highlights a broader issue in AI deployment: the “curse of the average.” When everyone uses the same AI tools with similar prompts, the output converges toward mediocrity. This is not a limitation of AI per se, but of how humans interact with AI—treating it as a magic solution rather than a tool requiring skilled input.

The resume problem mirrors challenges in AI-assisted coding, writing, and design. The difference between average and exceptional AI output is the quality of human input. This has implications for AI tool design: future tools should guide users toward specificity rather than accepting generic prompts.

My Take (🎯 Personal Analysis): This is a must-read for anyone using AI for professional tasks. The core insight—that AI reflects the average of its training data—is crucial. To get exceptional output, you must provide exceptional input. The resume example is perfect because it’s a high-stakes use case where generic output is costly.

For AI tool developers, this suggests an opportunity: build “specificity engines” that force users to provide unique context before generating output. For users, the lesson is clear: invest time in crafting detailed prompts. The “garbage in, garbage out” principle applies more strongly to AI than any previous technology. I’d add that the same logic applies to AI-powered code generation, content creation, and data analysis. The best AI users will be those who master the art of precise, contextual prompting.

📊 Market & Trends

The Great AI Consolidation

Today’s news reveals three powerful trends reshaping the AI landscape:

1. Capital Efficiency Becomes Paramount Tesla’s earnings miss, Amazon’s GPAI cuts, and OpenAI’s $750 billion forecast form a coherent narrative: the market is bifurcating between capital-rich players who can afford the infrastructure arms race and those who cannot. Tesla’s negative free cash flow suggests even well-capitalized companies can overextend. Amazon’s retreat from general-purpose models signals that only a handful of players—OpenAI, Google, Meta, Anthropic—can sustain horizontal model development. The rest must specialize or partner.

2. Sovereign AI Emerges as a Geopolitical Force Solar Open 2’s release, timed with SpaceXAI’s Texas expansion and OpenAI’s US-centric data center plan, highlights the geopolitical dimensions of AI infrastructure. Countries are racing to build domestic AI capabilities, not just for economic reasons but for strategic autonomy. Expect similar announcements from Japan, India, the EU, and Saudi Arabia in the coming months. This will drive demand for open-source models and localized data centers.

3. Agentic AI Defines the Next Frontier Solar Open 2’s agent-optimized architecture, NotchAgent’s macOS integration, and RoleFrame’s resume analysis all point toward agentic AI as the dominant paradigm. Foundation models are becoming commodities; the value is shifting to systems that can act autonomously—browse the web, use APIs, control hardware, and execute multi-step tasks. This creates opportunities for middleware, tooling, and integration platforms.

Market Indicators

🔮 Looking Ahead

Predictions

  1. Tesla will raise capital by Q1 2027: At current cash burn rates, Tesla’s $18 billion cash reserve will drop below $10 billion by mid-2027. Expect a $5-10 billion convertible bond offering, likely tied to AI milestones.

  2. OpenAI’s $750 billion forecast will trigger regulatory scrutiny: The scale of planned investment will attract antitrust and national security reviews. The US government may mandate that some data centers use domestic chip manufacturing, benefiting Intel and TSMC’s Arizona fab.

  3. Solar Open 2 will be adopted by Korean government agencies within 6 months: The sovereign AI narrative aligns with Korea’s digital sovereignty goals. Expect announcements from the Ministry of Science and ICT and major Korean conglomerates (Samsung, LG, Hyundai).

  4. Amazon will acquire an AI startup within 12 months: With GPAI cuts freeing up resources and the need for domain-specific AI, Amazon will likely acquire a company focused on vertical AI (healthcare, logistics, or supply chain).

What to Watch Next Week

Emerging Themes

💻 Code & Tools Spotlight

NotchAgent – Native macOS Notch Control for AI Agents

# Installation via Homebrew
brew tap ykushch/homebrew-agsig
brew install agsig

# Start the NotchAgent service
agsig --daemon

# Test with curl
curl -X POST http://localhost:8080/notch \
  -H "Content-Type: application/json" \
  -d '{
    "content": "AI Agent Active",
    "style": "animated",
    "duration": 3000
  }'

# Python agent integration example
import requests

def update_notch(status):
    response = requests.post(
        "http://localhost:8080/notch",
        json={
            "content": status,
            "style": "static",
            "duration": 5000
        }
    )
    return response.status_code == 200

# Use in your AI agent
update_notch("Processing FSD data...")

Solar Open 2 – Quick Start

# Requires transformers >= 4.45.0
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "upstage/solar-open-2-314b",
    device_map="auto",
    torch_dtype="bfloat16"
)
tokenizer = AutoTokenizer.from_pretrained("upstage/solar-open-2-314b")

# Agent-optimized inference
prompt = """You are an AI assistant with access to tools.
User: Find the latest Tesla stock price and calculate its P/E ratio.
Assistant: I'll use the stock price API and financial data tool."""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    tool_use=True  # Enable native tool calling
)
print(tokenizer.decode(outputs[0]))

This report was generated by Smartotics AI Research on 2026-07-23. All news items are sourced from real events. Data points are based on publicly available information and may contain forward-looking statements.

This report is based on real news collected from Hacker News, GitHub Trending, 36Kr, and Product Hunt.

Sources Referenced:

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