Fisker => open source car company

 Fisker went bankrupt and owners built open source car company from the ashes | Electrek

When Fisker Inc. filed for Chapter 11 bankruptcy in June 2024, it left roughly 11,000 Ocean SUV owners holding the keys to vehicles that cost them anywhere from $40,000 to $70,000 — and that were rapidly losing the software brains that made them work. No more over-the-air updates. No more connected services. No more warranty. The manufacturer was dead.

What happened next is one of the most remarkable stories in the history of the electric vehicle industry. Instead of accepting that their cars would become rolling paperweights, Fisker Ocean owners organized, reverse-engineered their vehicles’ proprietary software, hacked into CAN bus networks, built open-source tools on GitHub, and effectively stood up a volunteer-run open-sourced car company from the ashes of Fisker.

Within months of the bankruptcy filing, thousands of Ocean owners formed the Fisker Owners Association (FOA) — a nonprofit that quickly grew to 4,000 members and began operating as something between a car club, a tech startup, and an independent automaker.

 

AI: export Google AI Studio chats

 in google ai studio, is there a way to copy/export whole conversation?

---

Currently, Google AI Studio does not have a native "Export" or "Download" button for saving your conversations in readable formats like PDF or Markdown.

Furthermore, simply hitting Ctrl+A or Cmd+A to copy the page manually usually fails on longer chats. This is because AI Studio uses "virtual scrolling," meaning the interface only loads the chat bubbles currently visible on your screen.

However, there are three practical ways to export or copy your entire conversation, depending on how you need the data:

1. The Best Method: Use a Browser Extension (Third-Party)

Because this is a major pain point for developers, the community has created free Chrome extensions specifically designed to bypass the virtual scrolling limitation and extract the full chat.

• Google AI Studio Exporter or Chat Exporter for Google AI Studio: These are free extensions in the Chrome Web Store. When clicked, they automatically scroll through your entire chat to capture every message and let you download the conversation as a clean Markdown (.md), PDF, or plain text file.
• Gemini to PDF: Another popular community extension that adds a "Save Chat" button to export your conversations preserving formatting, code blocks, and tables.

2. The Quick Native Workaround: The "Get Code" Button

If you don't want to install an extension and just need the raw text of the entire conversation, you can use the built-in code generator:

1. Click the Get Code button at the top right of the AI Studio interface.
2. Switch the tab to cURL, JSON, or Python.
3. Inside the code snippet, look for the contents or history array. This contains your entire back-and-forth conversation in a structured text/JSON format.
4. You can copy this entire block of code to your clipboard. While it includes coding syntax, it ensures no part of the conversation is cut off.

3. The Backup Method: Download the JSON from Google Drive

If you have "Auto-save" enabled, Google AI Studio automatically backs up your chats to your Google Drive.

1. Go to your Google Drive and locate the saved AI Studio file (it usually has the blue/white AI Studio butterfly icon).
2. If you double-click it, it will just reopen AI Studio.
3. Instead, right-click the file and select Download.
4. This will download a .json file containing the complete chat history, prompt configurations, and system instructions, which you can open in any text editor or parse with a script.

AI "SaaSpocalypse"

SaaSpocalypse - Wiktionary, the free dictionary

A predicted (and disputed) implosion of established SaaS development companies as a result of emerging commercialized AI being fundamentally disruptive to the industry.

Some investors are quick to dump SaaS stocks because they predict a SaaSpocalypse, whereas others believe that AI is changing how SaaS companies do their work without making those companies unnecessary or unviable.

Related terms AIpocalypse

 Trump-Xi Summit, Benioff: "Not My First SaaSpocalypse," OpenAI vs Apple, Multi-Sensory AI, El Niño - YouTube

The conversation highlights several critical insights regarding the current state and future of artificial intelligence within the software and technology sectors:

• The 'SaaSapocalypse' and Rerating: The enterprise software market is undergoing a significant correction, often described as a 'SaaSapocalypse.' While top software companies are still posting strong quarterly results, their valuations have been rerated downward as markets adjust their expectations in light of the AI revolution.
• The Shift to Agents and Platforms: There is a fundamental shift occurring from traditional, static software to more dynamic systems. Businesses are moving toward headless platforms, where AI agents interoperate to automate complex tasks, such as outbound sales and customer service, far more efficiently than previously possible.
• The Importance of Context and Data: AI models are probabilistic and require high-quality, 'grounded' data to be effective in an enterprise setting. Integrating data across platforms (e.g., Salesforce, Slack) is essential for AI to act as a reliable 'world model' for businesses.
• The Evolution of Coding: Coding has become vastly more efficient due to generative AI, enabling non-technical users to build and interact with complex systems. However, this has also led to intense competition for talent and a race among AI labs to pivot toward the most effective tools, with a current focus on coding agents.
• Token Efficiency: While there is fear that the adoption of real-time, multi-sensory AI models will lead to unsustainable costs, there is a counter-argument that current token usage is inefficient. The industry is expected to develop intermediary layers to route tasks to the most affordable model available rather than defaulting to the most expensive ones.
• Hardware and Local Models: There is a strong trend toward localized AI running on high-end hardware, such as new Mac processors. This approach provides a clear path to maintaining privacy and reducing reliance on cloud-based AI, which faces growing trust issues.

stock price charts can be misleading, depending how one looks at them... context matters

Architecture, AC without HVAC? "cross ventilation"

This FREE Hack Will INSTANTLY Cool Your Home By 42F — Here’s How. - YouTube

This video explores the science and history of cross ventilation, a design principle used for centuries by civilizations in the Middle East, Africa, and Asia to create survivable indoor climates in extreme heat. We dive into the physics of the Bernoulli principle, explain how architectural icons like the Iranian Badgir and the Southern Shotgun house acted as natural wind tunnels, and reveal why modern building codes have moved away from these efficient designs. 

Whether you're looking to install a whole-house fan or simply want to know how to reposition your furniture and windows to drop your indoor temperature by up to 6∘ F, this guide provides the tools to outsmart the summer heat without touching your thermostat.

AI: Dario Amodei & Marc Benioff, Anthropic & Salesforce

very informative and uplifting chat

Dario Amodei & Marc Benioff: Future of AI - YouTube @ Salesforce Events - YouTube

Dario Amodei | LinkedIn

In this discussion, Dario Amodei co-founder and CEO of Anthropic,

and Marc Benioff, founder and CEO of Salesforce, and early investor in Anthropic,

cover the evolution of Anthropic, the future of AI, and its integration into the enterprise.

Key Points:

• Founding Philosophy: Dario Amodei and his seven co-founders, including his sister Daniela Amodei, left OpenAI to establish Anthropic with a core focus on safety, responsibility, and the development of reliable models for enterprise use cases.
• The Evolution of Coding: A significant shift is occurring where AI models are now capable of writing large portions of code. Rather than replacing software engineers, this is creating a shift toward rebalancing roles where engineers act as supervisors and editors, significantly increasing their productivity.
• Agentic Capabilities: The industry is moving from simple text-generation toward "agentic" models that can perform end-to-end tasks, such as managing workflows or analyzing complex medical data, acting more like autonomous coworkers.
• Enterprise Transformation: Anthropic is positioning itself to serve the enterprise world, focusing on industries like finance, healthcare, and insurance, where trustworthiness and reliability are paramount.
• Infrastructure and Compute: The massive demand for compute power to support scaling laws remains a primary driver for data center expansion, though Amodei cautions that public discourse around data center deals may sometimes be inflated by "double counting."

Notable Quotes:

• "Building ordinary software, it's like building a skyscraper...
  This (AI) is ironically a bit more like biological. It's organic...
  You're growing these models." — Dario Amodei
• "What is an enterprise? It's kind of a super intelligence. It is an entity that acts in the world in a way with a level of strategy and knowledge and power that goes far beyond what any individual human can do." — Dario Amodei
• "I'm optimistic about complementarity in the short run. I do worry as we get out to 2 years, 5 years that across the economy... we'll have a kind of labor disruption that is not fundamentally different from what we've seen with previous technologies but that operates faster." — Dario Amodei

Marc Benioff Said Salesforce Will Spend $300M on Anthropic Tokens - Business Insider

The Making Of Dario Amodei - by Alex Kantrowitz

The profile explores the motivations, background, and business trajectory of Dario Amodei, the co-founder and CEO of Anthropic. It highlights how a personal tragedy—the loss of his father to a rare illness that became largely curable just a few years later—fuels his urgent drive to accelerate AI development to solve massive biological and human problems. Despite being labeled by some as an industry "doomer" due to his outspoken stance on safety and regulation, Amodei considers himself a capability bull who advocates for safety frameworks specifically so that development doesn't have to slow down.

---

Key Points

• The Catalyst: Amodei’s shift from theoretical physics to biology, and eventually to AI, was profoundly shaped by his father’s death in 2006. He believes faster technological and scientific progress could have saved his father, driving his current short timeline for AI advancement.

• Pioneering Scaling Laws: During his time at Baidu in 2014, Amodei contributed to the formulation of AI "scaling laws"—the principle that predictably increasing data, compute, and model size yields better performance without needing entirely new methodology. He remains one of the industry's purest believers in this exponential growth curve.

• The OpenAI Split: Amodei led the landmark GPT-3 project at OpenAI. However, systemic disagreements over safety, commercialization, and corporate governance led Amodei and a core group of researchers (referred to as "the pandas") to break away and found Anthropic in late 2020.

• Massive Revenue Growth: Anthropic has become one of the fastest-growing software companies in history. Its annualized recurring revenue rocketed from $1.4 billion in March 2025 to roughly $4.5 billion by July 2025, primarily driven by businesses utilizing its models via API integrations and specialized coding tools like Claude Code.

• Financial & Safety Pressures: Despite massive revenue, Anthropic is deeply unprofitable, with projected losses of $3 billion for the year due to immense computing and training costs. To keep scaling, the company has raised nearly $20 billion from tech giants like Amazon and Google, and is exploring a new $5 billion round that includes Middle Eastern Gulf state investors.

• "Race to the Top" Strategy: Amodei pushes for strict export controls, safety regulations, and public stress-testing frameworks (like Anthropic's Responsible Scaling Policy). Rather than trying to create regulatory capture to shut out open-source competitors like DeepSeek, Amodei asserts he is trying to force an industry-wide upward trajectory in safety practices as models approach human-level knowledge work.

js tool: FluidCAD: Parametric CAD

 Parametric CAD for everyone | FluidCAD

Write JavaScript. See 3D geometry in real time.

Fluid-CAD/FluidCAD: Parametric cad modeling with Javascript @GitHub

TS, LGPL

antfly: AI DB in Go

Building a Distributed Search Engine in Pure Go — Antfly Blog

Antfly is a distributed document database and search engine written entirely in Go. It combines full-text search (BM25), vector similarity, sparse vectors (SPLADE), and graph traversal in a single binary. It also ships with built-in ML inference for embeddings, reranking, and chunking — so you can go from raw documents to production RAG without reaching for external APIs.

Run antfly swarm and you get a fully functional node with search, ML inference, a dashboard, and an MCP server — all in one process. Need to scale? Add more nodes and Antfly handles shard splitting and replication automatically.


antflydb/antfly @GitHub

Antfly is a distributed search engine built on etcd's raft library. It combines full-text search (BM25), vector similarity, and graph traversal over multimodal data — text, images, audio, and video. Embeddings, chunking, and graph edges are generated automatically as you write data. Built-in RAG agents tie it all together with retrieval-augmented generation.

Go 1.26 introduced the experimental simd/archsimd package, giving Go programs native access to SIMD instructions. Antfly uses go-highway — a portable SIMD library inspired by Google's Highway — to write vector operations once and run them on AVX2, AVX-512, ARM NEON, or a pure Go fallback.

MDN: React => Markdown & Web Components

 Under the hood of MDN's new frontend

EV: Tesla Model S legacy

 Tesla Never Stopped Developing The Model S — Revelations with Jason Cammisa - YouTube

The Tesla Model S: A Paradigm Shift

• Industry Transformation: The Model S is widely regarded as the most significant car of the last 75 years, having catalyzed the global shift toward electrification and forced traditional automakers to rethink their approach to vehicle development.
• Constant Iteration: Unlike traditional manufacturers that freeze designs for years, Tesla continuously updated the Model S with thousands of hardware and software improvements throughout its 14-year lifespan. As a result, the final version shares only about 3% of its parts with the original 2012 model.
• Software-Defined Vehicle: The car pioneered over-the-air (OTA) updates, allowing Tesla to fix issues, improve efficiency, and add new features—such as Sentry Mode—long after the customer had taken delivery. This redefined the car as a living, evolving piece of technology rather than a static product.
• Efficiency and Performance: By focusing on "battery bucks"—optimizing every component to save weight and electricity—Tesla engineers dramatically increased efficiency and performance over time. The modern Plaid variant is significantly faster, more efficient, and better equipped than early models, all while remaining competitively priced when adjusted for inflation.
• Challenging Conventions: While early models faced criticism for build quality, the Model S proved that consumers valued technological innovation, usability, and performance over traditional manufacturing metrics like panel gaps. It set the standard for what a desirable EV could be, paving the way for the Model Y to become the world’s best-selling passenger car.
• A Legacy of Progress: The video argues that traditional automakers who abandoned or delayed their EV programs in response to short-term market fluctuations face an existential risk. Tesla’s success has established a new industry reality where rapid iteration, software integration, and a focus on long-term efficiency are essential for survival.

Go => C; => GoLang

 Solod

solod/doc/spec.md at main · solod-dev/solod

Solod (So) is a strict subset of Go that translates to regular C — with zero runtime, manual memory management, and source-level interop.

• Go in, C out. You write regular Go code and get readable C11 as output.
• Zero runtime. No garbage collection, no reference counting, no hidden allocations.
• Everything is stack-allocated by default. Heap is opt-in through the standard library.
• Native C interop. Call C from So and So from C — no CGO, no overhead.
• Go tooling works out of the box — syntax highlighting, LSP, linting

ubavic/compiles-to-go: List of programing languages that compile to Go @GitHub

Go’s simplicity, fast compilation, and mature ecosystem make it an appealing compilation target for new languages. This is a list of languages that, in one way or another, compile down to Go assembly.

Golang Weekly Issue 596: April 10, 2026

A Fast Immutable Map in Go – Daniel Lemire's blog

x1unix/go-playground: Better Go Playground with autocompletion and syntax highlight

Better Go Playground

AI: tensors vs vectors

What is a Tensor? An Animated Introduction! - YouTube

3Blue1Brown - YouTube

Vespa AI and Surpassing the Limits of Vector Search - Software Engineering Daily

Vespa

RAG at Scale: Why Tensors Outperform Vectors in Real-World AI | Vespa Blog

The blog post from Vespa.ai argues that while vector databases are the current standard for AI retrieval, they are becoming insufficient for complex, real-world RAG (Retrieval-Augmented Generation) applications. The author proposes tensors as a more powerful alternative because they preserve data structure and relationships that flat vectors lose.

---

The Problem with Vector-Only Systems

The post identifies several bottlenecks when using standard vector databases at scale:

• Lack of Hybrid Support: Difficulty matching exact phrases, boolean logic, or keyword expressions alongside semantic search.

• Weak Logic Integration: Hard to combine unstructured content with structured metadata (like price, date, or category).

• External Latency: Relying on external re-rankers or ML inference services introduces complexity and "fragile" pipelines.

• Flattened Context: Vectors represent data as a single embedding, losing sequence, position, and spatial relationships.

---

Why Tensors are Superior

Tensors act as a generalization of vectors (a vector is a 1D tensor), allowing for multidimensional data representation.

Feature	Vector Representation	Tensor Representation
Data Structure	Flattened (single embedding)	Structured (multi-embedding)
Dimensions	Anonymous/Implicit	Named (e.g., "time", "patch", "batch")
Capability	Semantic similarity only	Reasoning, ranking, and spatial context
Use Case	Basic search	ColBERT, ColPali, and multimodal search

---

Vespa’s Tensor Framework Principles

Vespa uses a specific "tensor formalism" designed for high-performance production environments:

1. Unified Dense & Sparse Support: Allows mixing fixed-size numeric vectors (dense) with symbolic tags or categories (sparse) in a single model.

2. Named Dimensions: Replaces confusing axis indices (axis=0) with semantic labels, making code safer and easier to debug.

3. Minimal & Composable Ops: A concise mathematical foundation that can express complex logic without a bloated API.

The Bottom Line

The future of AI applications requires more than just finding "similar" data; it requires reasoning. If vectors help a system retrieve information, tensors help the system understand and process it natively within the database engine.

For those interested in implementation, the post points toward The RAG Blueprint for building systems using this framework.

AI enabled CAD tool: Rayon

Rayon.design

AI-powered drawing for interior designers and architects.

Pricing | Rayon

in-security: TanStack npm supply-chain compromise

danger!

 Postmortem: TanStack npm supply-chain compromise | TanStack Blog

What the malware does?

When a developer or CI environment runs npm install, pnpm install, or yarn install against any affected version, npm resolves the malicious optionalDependencies entry, fetches the orphan payload commit from the fork network, runs its prepare lifecycle script, and executes a ~2.3 MB obfuscated router_init.js smuggled into the affected tarball. The script:Harvests credentials from common locations: AWS IMDS / Secrets Manager, GCP metadata, Kubernetes service-account tokens, Vault tokens, ~/.npmrc, GitHub tokens (env, gh CLI, .git-credentials), SSH private keys

 TanStack & MANY more packages affected - a deep dive & analysis - YouTube by MaxS

This video discusses an active and large-scale supply chain attack targeting the TanStack ecosystem, which has since spread to other npm packages and the Python ecosystem. The malware functions as a worm, harvesting sensitive credentials like npm tokens, GitHub tokens, and AWS secrets from compromised machines, then using them to publish further malicious packages.

Key takeaways:

• Attack Vector: The attackers exploited the GitHub Actions "pull request target" pattern, poisoning the project's cache to inject malicious code into the CI/CD pipeline of the base repository.
• Exfiltration and Propagation: Once inside a CI/CD workflow, the malware steals short-lived tokens, allowing the attacker to publish malicious versions of packages automatically.
• Security Recommendations:
  • Avoid installing new packages while the attack is ongoing.
  • Run development environments in isolated virtual machines or dev containers rather than directly on your host system.
  • Use secure secret management (e.g., Doppler or Infisical) instead of storing raw credentials on disk.
  • Configure package managers (like bun or pnpm) to block post-install scripts and enforce minimum package release age policies.
• Current Status: While the TanStack maintainers identified and contained the threat quickly, the attack is still propagating to other ecosystems. Security best practices, such as monitoring network traffic and reviewing CI/CD workflows, are essential for defense.

And similar cheating is happening with AI, and with pre-trading AI stocks, too...
Nothing is safe and secure anymore?

this is really bad... - YouTube MaxS

Wait until AI agents get compromised... - YouTube MaxS

WTF is going on at Anthropic?! - YouTube by MattB.

The Internet Was Weeks Away From Disaster and No One Knew - YouTube by Veritasium

book: The Nature of Code (with Processing graphics library)

AxiomeCG/awesome-threejs: 3️⃣ A curated list of awesome ThreeJS resources

The Nature of Code: Amazing book by a very funny author about a bunch of stuff to make experiences more natural based on the Processing language by @shiffman

The Nature of Code: Simulating Natural Systems with JavaScript: Shiffman, Daniel: 9781718503700: Amazon.com: Books

"All aboard The Coding Train! This beginner-friendly creative coding tutorial is designed to grow your skills in a fun, hands-on way as you build simulations of real-world phenomena with “The Coding Train” YouTube star Daniel Shiffman.
What if you could re-create the awe-inspiring flocking patterns of birds or the hypnotic dance of fireflies—with code?"

Processing - Wikipedia

Processing is a free graphics library and integrated development environment (IDE) built for the electronic arts, new media art, and visual design communities with the purpose of teaching non-programmers the fundamentals of computer programming in a visual context.

Processing uses the Java programming language, with additional simplifications such as additional classes and aliased mathematical functions and operations. It also provides a graphical user interface for simplifying the compilation and execution stage.

Processing Foundation @GitHub

processing/p5.js: p5.js is a client-side JS platform that empowers artists, designers, students, and anyone to learn to code and express themselves creatively on the web. It is based on the core principles of Processing. Looking for p5.js 2.0? http://beta.p5js.org

Examples

HW business: Intel comeback

with "fab" deals with SpaceX, Apple... 

 HTML vs. Markdown, Meta AI Backlash, & Data Center Wars

Apple Nears Intel Chip Deal to Cut TSMC Reliance
Apple has reached a preliminary manufacturing agreement with Intel, according to the Wall Street Journal — a notable shift for a company that has relied exclusively on TSMC for over a decade. The deal would help Apple diversify chip supply amid ongoing constraints that the company says held back iPhone and Mac sales in its March quarter. Intel, now under CEO Lip-Bu Tan, is aggressively courting external customers for its foundry business; its stock jumped more than 15% on the news. Bloomberg separately reported Apple is also exploring a similar arrangement with Samsung.

 Lip-Bu Tan | LinkedIn

CEO of Intel Corporation, Chairman of Walden International, Founding Managing Partner of Walden Catalyst Ventures

Intel: INTC Stock Price Quote & News | Robinhood

intel stock price - Google Search

Intel, SK hynix shares surge following reports of chip packaging partnership — SK is said to be testing Intel's 2.5D EMIB for HBM integration | Tom's Hardware

AI Fleetio: fleet maintenance Service Advisor

many business are scrambling to figure out useful use-cases for AI... 

Fleet Management Software to Run Your Fleet Smarter

AI Service Advisor | Fleetio

"Service Advisor doesn't wait for you to catch a problem. It scans each repair order the moment it hits Fleetio, flagging cost spikes, duplicates, and out-of-policy work. You get a short list of items worth your judgment, while the routine work keeps moving."

AI IDEs pricing: GH Copilot, Claude, Cursor, Antigravity

GitHub Copilot · Your AI pair programmer

Pro: $10/mon

Copilot cloud agent
Copilot code review
Claude and Codex on GitHub and VS Code
300 premium requests, with the option to buy more1
Unlimited agent mode and chats with GPT-5 mini2
Unlimited inline suggestions
Access to models from Anthropic, Google, OpenAI, and more

EV: Rivian R2, Fleets, VW ID1, Uber FSD, Also Doordash

 Inside Rivian’s Future with CEO RJ Scaringe | R2 Updates, Fleet Strategy & ACT Expo - YouTube

In this conversation at ACT Expo, Rivian CEO RJ Scaringe discusses the company's multi-faceted fleet strategy, which balances commercial delivery, autonomous ride-hailing, and micro-mobility services. Rivian is leveraging common horizontal technology platforms—including autonomy, connectivity, and diagnostic software—across all its vehicle types to drive efficiency and scalability.

Key Takeaways:

• Fleet Diversity: Rivian is expanding beyond its core electric delivery van (EDV) partnership with Amazon into new areas, including a robotaxi venture with Uber based on the R2 platform and a micro-mobility partnership with DoorDash for delivery bots.
• Technology Integration: The company has shifted from "software-defined" to "AI-defined" vehicles, using onboard data to improve predictive maintenance, service intervals, and operational decision-making.
• Scalability and Cost: The R2 platform is critical for Rivian’s move into the mass-market mid-size SUV segment. By consolidating computing power into zonal architectures and designing electronics in-house, Rivian is significantly reducing harness complexity and production costs.
• Strategic Partnerships: Rivian has entered a $5.8 billion software licensing deal with Volkswagen Group. This collaboration aims to leverage Rivian's zonal computing and software expertise to modernize the electronic architecture of future Volkswagen vehicles, starting with the ID1

Rivian Universal Hands-Free And Autonomy+: How Much Does It Cost?

DoorDash partners with Rivian spinoff Also for autonomous delivery vehicles