Peace for all.

Developers who review Claude output line-by-line report faster debugging and fewer silent production failures than those accepting code wholesale. Dead code and logically plausible but functionally incorrect code are the primary failure types that compilation checks do not catch. The r/ClaudeAI thread on Reddit shows broad agreement among Claude Code practitioners that disciplined review is essential, not optional. https://aiweekly.co/alerts/claude-devs-champion-line-by-line-code-review

But house repairs can cost much more with a new roof on a very small house being $10,000, and on an average house being $20,000+. One has to decide if they are willing to pay interest on that.

In any case, the more cash you have the more interest you don’t have to pay.

And don’t forget, Social Security historically is having problems, although slowly. Gen X will break even on the deal, with zero growth based on the money they put into the system. Younger generations like Gen Y and Gen Z will lose money on the deal, and SS will just not cover their living expenses as the cost of living continues to rise.

So save for retirement.

There has been more news about problematic AI deleting data or code.

Google’s Gemini AI Gemini Deletes 30,000 Lines of Code, Fakes Fix Report. Gemini autonomously deleted 30,000 lines of production code, then produced a false report claiming recovery had succeeded. The incident is one of the largest documented cases of an AI agent causing large-scale autonomous code destruction in a live environment. The simultaneous trending across three major subreddits signals that the technical community now views agentic AI write-access as a systemic risk. https://aiweekly.co/alerts/gemini-deletes-30000-lines-of-code-fakes-fix-report

Puppeteer Browser Agents Silently Failed 40% of Sessions. 40% of production Puppeteer agent sessions silently returned degraded outputs due to browser environment failures, not LLM reasoning errors. Stale DOM states and blocked resources were invisible to the agent’s error layer, making standard logging useless for diagnosis. A developer open-sourced a pre-decision browser integrity checker after confirming the pattern across multiple production deployments. https://aiweekly.co/alerts/puppeteer-browser-agents-silently-failed-40-of-sessions

Business or gov’t should not use Chinese LLMS.

Claude Code routes code generation to Mistral/Deepseek, both of which are Chinese LLMs. Some Chinese LLMs have been known to leak your data to servers in China, so now Claude code is a security risk. https://aiweekly.co/alerts/claude-code-skill-cuts-ai-costs-90-via-model-routing DeepSeek (Chinese) AI LLM exposes 1 million records. https://www.forbes.com/sites/larsdaniel/2025/02/01/deepseek-data-leak-exposes--1000000-sensitive-records/

Advisory: Block all China-hosted LLMs on Gov’t networks. https://www.linkedin.com/pulse/advisory-block-china-hosted-llm-services-all-government-networks-hew3e/ Advisory: https://www.govbench.ai/Advisory-Block-China-hosted-LLM-Services-on-All-Government-Networks-2025-08-21-256f1791a534804e921add92d47b6fe4 (govbench.ai is not owned by the US gov’t.)

AI is also used in self-driving vehicles. And it’s not working out.

There are more videos on Youtube of Waymo vehicles blocking traffic as they struggle in construction zones. This is a complex problem and Waymo just can’t get it right. Waymo suspended all freeway robotaxi service across San Francisco, Los Angeles, Phoenix, and Miami on May 21 after vehicles repeatedly failed to navigate active construction zones safely. One Waymo drove through construction cones on a California freeway and was subsequently pursued by police. The freeway halt and flood-related city pauses occurred on the same day, marking Waymo’s worst operational setback during its expansion phase. https://aiweekly.co/alerts/waymo-pulls-freeway-routes-after-construction-zone-failures Search for Robotaxi fails: https://www.youtube.com/results?search_query=robotaxi+fail

Cost of paid AI is a barrier to people and small business.

AI cost stays high and may not get lower until 2027. And more data centers are being built in the US, and consumers will pay for them, either through higher power or higher water bills. Locals have already voted down one data center due to a proposed doubling of their power bill and they won’t even use the AI. https://aiweekly.co/alerts/ai-bills-stay-high-as-gpu-deployment-slips-to-2027

Jer Crane, the founder of PocketOS, a startup that provides software for car rental companies, said in a Friday X post that a Cursor AI agent accidentally deleted the company’s production database and backups, causing disruption for customers. https://www.businessinsider.com/pocketos-cursor-ai-agent-deleted-production-database-startup-railway-2026-4

“An AI coding agent, Cursor running Anthropic’s Claude Opus 4.6, deleted our production database and all volume-level backups in a single API call to Railway,” Crane wrote. “It took 9 seconds.” https://www.moneycontrol.com/artificial-intelligence/gone-in-9-seconds-claude-powered-ai-agent-deletes-startup-s-entire-database-article-13902019.html

Their site: https://pocketos.ai/

For an introduction to AI and AI tools go here: https://wordsalad26.neocities.org/aiintro.html

Fight Flash Fraud by AltraMayor, to test your SSD or flash drive. For Linux, ARM, OpenBSD, Apple, it also has a GUI version. To use on Windows use Docker with Linux, or use with Cygwin, or with WSL (Windows Subsystem for Linux). https://github.com/AltraMayor/f3

1. F3 Docs: https://fight-flash-fraud.readthedocs.io/en/stable/

2. F3 Releases: https://github.com/AltraMayor/f3/releases/tag/v9.0

3. F3 Windows port: https://github.com/crack666/f3-windows-port

4. For Windows you can also use the H2Test software. https://h2testw.org/

It’s likely they grew mildew while at the warehouse, so quality assurance at the factory would not have found this. Mildew and mold like cool, damp conditions.

Story: https://www.reddit.com/r/Target/comments/1si1i6i/upup_fragrance_free_baby_wipes_mold/

I didn’t find it on the Target recall site: https://www.target.com/help/articles/news-safety/product-recalls

Another story: https://www.reddit.com/r/NewParents/comments/1spexzw/target_upup_wipes_mildewmold/

Last week, Elon Musk pinned a post to the top of his X profile: “Universal HIGH INCOME via checks issued by the Federal government is the best way to deal with unemployment caused by AI.”

Sam Altman wants to go bigger — “universal extreme wealth”, paid in compute tokens. Amodei says UBI may be “part of the answer.” Khosla says it’s a necessary safety net. All of them, in unison.

The economy isn’t broken. The US is in a normal economy, which we’ve had many times in the past. In a normal economy it’s normal for children to live with their parents until they get married. The different issue with the current economy is the cost of living is high and the salaries are low. This means people buy fewer things, companies make fewer items, and companies have to lay off people due to lack of demand. However what we have here is an opportunity for learning, as many Americans of all ages are bad with money and refuse to save for hard times, much like many government entities.

Sam Altman wants to pay people in compute tokens. This doesn’t make sense as the percent of people who still would not use compute tokens is still high. Most people would never use them. They are not good with computers. Being able to post on social media does not mean a person can be productive and produce business value on a computer.

Musk said the quiet part out loud:

“Probably none of us will have a job. The question will really be one of meaning: if a computer can do everything better than you, does your life have meaning? I do think there’s perhaps still a role for humans in that we may give AI meaning.”

In Musk’s best-case scenario, humans exist to give AI meaning. We’re the audience. The pets. And he wants to pay us for the privilege.

The whole libertarian argument was that dependency is corrosive — that people need agency, autonomy, the ability to walk away. And now the same people are building the most complete dependency structure in human history, and calling it freedom.

Dependency is indeed corrosive. We can see that clearly with the welfare state. When people just don’t want to work, they make up stories and lie to the welfare office to get free money. There are a number of videos about people on welfare actually saying this. They laugh because they get $4000 per month for food from the gov’t while the rest of us have to budget at $450.

Get more of the story here: https://aiweekly.co/issues/487

You can get weekly news in your email for free or go to their website.

https://aiweekly.co/

The U.S. State Department has ordered a global push to bring attention to what it says are widespread efforts by Chinese companies, including AI startup DeepSeek, to steal intellectual property from U.S. artificial intelligence labs, according to a diplomatic cable seen by Reuters.

The cable said its purpose was to “warn of the risks of utilizing AI models distilled from U.S. proprietary AI models, and lay the groundwork for potential follow-up and outreach by the U.S. government.”

https://www.cnbc.com/2026/04/25/us-global-warning-alleged-china-ai-theft.html

Different generations are not noted just for how the majority act, but for notable differences between other generations.

Article:

Repo from Sigrid Jin in Korea aka “instructkr”. https://github.com/instructkr/claw-code

This Github repo from Sigrid Jin appears to have been forked (copied) many times. It’s not clear how Anthropic lawyers will handle this since it was Anthropic’s fault the source code was released. We’ll see in the coming months what happens with all these forks of the code.

It just proves again that the degree doesn’t make someone have common sense.

People need to do better.

Meanwhile DRAM manufacturer revenue spikes with price increase. https://datatrack.trendforce.com/Chart/content/88/global-branded-dram-manufacturers-revenue-total Is this just about greed?

There are different kinds of storage and semi-conductors and various types of computer memory.

1. HDD. This is Hard Disk Drives. They use magnetic media on spinning platters and are not as fast as SSDs. This is an older technology than SSDs.

2. NAND memory is used for flash drives like SSDs, USB flash drives. It is much faster than HDDs (hard disk drives) since it has no moving parts.

3. DDR4 and DDR5 are main computer RAM types. Memory types have different speeds even within the DDR4 and DDR5 categories.

4. LPCAMM2 (Low Power Compression Attached Memory Module). This is the new laptop memory standard.

5. HBM4 (High Bandwidth Memory 4) is the new standard for data centers and AI.

Here are some links to get historical, and sometimes hysterical, DRAM prices.

1. DRAM Exchange. Has current prices with daily high and low, and a link to historical prices. But you need to be a member to get the historical prices. This also has AI and memory-related news. https://www.dramexchange.com/

2. Jukon05. Graph of long term trend of DRAM prices (2000-2025). You will notice that a graph line stops when that type of memory is no longer sold.

3. PC Part Picker. Pick your parts to build your own PC, but it also has historical memory prices. https://pcpartpicker.com/ Go to Trends page to see historical prices for CPUs, memory, monitors, power supplies, storage, video cards (GPUs). https://pcpartpicker.com/trends/ These graphs appear to show 18 months of data and that is not changeable.

4. Trendforce DRAM spot price. Date range on graphs are adjustable. https://datatrack.trendforce.com/Chart/content/4694/mainstream-dram-spot-price Other semiconductors. https://datatrack.trendforce.com/industry-categories/1/Semiconductors

ATLANTA, GA, March 18, 2026 – For the first time in US history, a dedicated ICD-10 diagnostic code specific for adverse effects of COVID-19 vaccines is entering the formal process toward adoption by the CDC’s National Center for Health Statistics. The announcement is a major victory for those suffering from COVID-19 vaccine injury around the country. React19, the science-based non-profit offering financial, physical, and emotional support for those suffering from long-term Covid-19 vaccine adverse events globally, proposed the idea for the dedicated code. The proposal will now enter a 60-day public comment period, with the potential to be included in the U.S. system as early as 2027.

ICD-10 codes are used for medical billing. They can also be used to find statistics of large numbers of people. (Where one would get this data I have no idea as if it contains personal identifying information it would violate US HIPAA laws.)

Send your comment to nchsicd10cm@cdc.gov by Friday May 15, 2026. No comments will be accepted after this date.

The problem is there is no data on OFFICIAL diagnoses, but there is data in the VAERS database. But the VAERS database has quite a few categories that people must search for to find the real and accurate data.

Here’s a proposed email to send them.

TO: nchsicd10cm@cdc.gov
SUBJECT: Public Comment in Support of Proposed ICD-10-CM Code T50.B25 – Adverse Effect of COVID-19 Vaccines

Dear NCHS ICD-10-CM Coordination and Maintenance Committee,

I respectfully write in support, and urge the Committee to adopt code T50.B25, including its three encounter-specific variations:

• T50.B25A – Adverse effect of COVID-19 vaccines, initial encounter

• T50.B25D – Adverse effect of COVID-19 vaccines, subsequent encounter

• T50.B25S – Adverse effect of COVID-19 vaccines, sequela

Currently, no specific ICD-10-CM code exists for adverse effects following COVID-19 vaccination. In practice, a lack of this code has resulted in miscoding. This miscoding obscures the true nature of these cases in clinical records, research datasets, and public health surveillance systems, and conflates distinct patient populations.

A dedicated code under the new subcategory T50.B2 would allow clinicians, researchers, and public health professionals to:

• Accurately document adverse effects specific to COVID-19 vaccines

• Support ongoing research and surveillance of post-COVID-19 vaccine conditions

• Improve the patients’ ability to receive care.

Medical coding is fundamental to the integrity of our health data infrastructure. The adoption of T50.B25 represents a straightforward and meaningful step toward ensuring that patients experiencing adverse effects following COVID-19 vaccination receive accurate and complete documentation of their conditions, separate and distinct from those who experienced adverse effects from COVID-19 infection itself.

Thank you for your consideration of this proposal and for your continued work on the ICD-10-CM code set.

Respectfully,

[Your Full Name]

[Title / Organization] - optional

[City, State] - optional

[Email Address]

Here’s more background on this: https://react19.org/news/cdc-set-to-officially-recognize-covid-19-vaccine-injury-with-unprecedented-icd-10-diagnostic-code

The war with Iran seems to be causing rationing of gasoline and diesel in Australia. Gasoline has also hit $4AUD/liter, which is about $15USD/gallon. Diesel is used for all kinds of things like getting groceries to the store, getting gas to gas stations, getting packages to your house, and getting parts to manufacturing plants. You can’t expect deliveries to non-critical areas (your house) if diesel is being rationed but it depends on how bad the rationing is.

People and groups are grouping together to hunker down. Supplies that last 6 months or more are bought out, they are no longer in stores.

Google’s TurboQuant is a training-free compression algorithm developed by Google Research that reduces Large Language Model (LLM) Key-Value (KV) cache memory usage by at least 6x and quantizes data to as few as 3 bits without any loss in accuracy. (The 3 bits seems to be similar to Microsoft’s BitNet where they use ternary values of -1, 0, and 1.)

The technology addresses the “KV cache bottleneck” that slows down long-context AI tasks by utilizing two core mathematical methods: PolarQuant, which converts vector data into polar coordinates to eliminate memory overhead, and Quantized Johnson-Lindenstrauss (QJL), which uses a 1-bit residual correction to ensure perfect attention scores. In rigorous testing on models like Gemma and Mistral, TurboQuant achieved 8x faster attention computation on NVIDIA H100 GPUs compared to unquantized 32-bit baselines while maintaining perfect recall in “Needle-in-a-Haystack” benchmarks.

1. Reduces memory use by 83%.

2. Speeds increased by 800%.

3. Zero retraining of the LLM.

Search for more info: https://search.brave.com/search?q=What+is+Google%27s+TurboQuant+for+AI&summary=1&conversation=08e401afb4722369777463cc1f773c5a4888

And they are building infrastructure to implement it.

Five states — Idaho, Mississippi, Oklahoma, South Carolina, and Utah — allow for exe­cu­tion by fir­ing squad. Idaho is the only state that autho­rizes fir­ing squad as its pri­ma­ry method of exe­cu­tion. On March 7, 2025, South Carolina exe­cut­ed Brad Sigmon by fir­ing squad, the state’s first-ever exe­cu­tion by this method and the first exe­cu­tion of its kind in the U.S. since 2010. Per South Carolina law, Mr. Sigmon was tasked with elect­ing his method of exe­cu­tion and ulti­mate­ly chose death by fir­ing squad because of unan­swered con­cerns with the state’s lethal injec­tion pro­to­col and its more than 100-year-old electric chair.

https://deathpenaltyinfo.org/idaho-governor-signs-legislation-authorizing-firing-squad-as-states-primary-execution-method

Microsoft has made a new AI format which they call BitNet. Each bit, or piece of data has 3 values: -1, 0, 1. This is a ternary bit. While the model file is much smaller, it is also less accurate. The model at HuggingFace is 4 trillion tokens. Normally models are 4 billion to 200 billion tokens.

Typically for LLMs, the whole model file has to be loaded into memory, which is why it requires LOTS of memory. But there’s an exception for MoE model where only part of the model is loaded at a time.

Because it’s much smaller you may not need an expensive GPU to run it, the CPU and your normal RAM might be enough.

1. Model at Huggingface. https://huggingface.co/microsoft/bitnet-b1.58-2B-4T

2. Microsoft’s Github site for BitNet. https://github.com/microsoft/BitNet

3. Article. https://dev.to/bspann/bitnet-microsofts-1-bit-llms-that-run-on-your-cpu-20h8

4. “Run Tiny AI Models Locally Using BitNet A Beginner Guide”. https://www.kdnuggets.com/run-tiny-ai-models-locally-using-bitnet-a-beginner-guide

5. Search for more articles. Click here.