I built a scikit-learn for RSI [P]
![I built a scikit-learn for RSI [P]](https://preview.redd.it/trkro8qdytyh1.gif?width=640&crop=smart&s=0d668e31132a75d0636098597f27bd70c96122b3 "I built a scikit-learn for RSI [P]") | After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search:
That motivated us to build Gear, an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI, making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluation. With GPT 5.6 Luna held at medium reasoning effort, we observed these task pass rates:
At max effort on Marketing, Luna with the optimized harness reached 88.88% objective completion, versus 84.08% for Astra + Codex. Strict task pass rates were 53% versus 57%, so the advantage was in partial completion. Separately, with both models using the same optimized DSH at max effort, Luna’s estimated API cost averaged $0.0568 per task versus $1.1858 for Astra—95.21% lower, excluding optimization and infrastructure costs. We’d welcome feedback on candidate evaluation and diversity preservation under limited budgets. [link] [comments] | |||||||||||||||
Surviving "The Graduate"
U.S workers just took home their smallest share of capital since at least 1947—and the lowest ever recorded
As corporate earnings soar and U.S. GDP balloons, the American workforce isn’t feeling the same boom. American workers are taking home less of the country’s overall income, data from the Bureau of Labor Statistics shows, and employment in the U.S. is set to continue to slow.
Labor share, or the portion of the U.S.’s economic output that workers receive through salary and wages, decreased to 52.8% in the second quarter of 2026, its lowest level since the BLS started recording this data in 1947, according to its labor productivity and costs report. In the previous quarter, labor share was at 53.6%. This decade, the labor share average was about 55.6%.
That’s despite corporate earnings skyrocketing, with profits for Fortune 500 companies hitting a record $2.1 trillion in 2025. The U.S. GDP grew 3.4% in the third quarter last year, exceeding economists’ predictions.
That growth has not only come at the expense of how much of the pie of wealth workers are taking home, but also how many Americans are in the workforce, economists warn.
“That decline in the share of labor has got to be either falling earnings or falling numbers of people,” Raymond Robertson, a labor economist at Texas A&M’s Bush School of Government, told Fortune. “The falling share of income is having to do with the shift towards capital.”
Indeed, there are growing signs that as national income balloons, the U.S. workforce is deflating. Unemployment ticked up to 4.2% in September, and employers added just 584,000 jobs in 2025 compared to 2 million added in 2024. The U.S. added 29,000 jobs in September, about one-third of economists’ projections.
The bifurcation of corporate victories and weak labor data raises concerns among economists of jobless growth jeopardizing the U.S. workforce, as well as a K-shaped economy, where the rich get richer while the poor get poorer, becoming more exaggerated.
“Data right now is very mixed,” Robertson said. “But I think it also all consistently points to this idea that things are getting worse for workers and much better for billionaires.”
What is causing jobless growth?
Robertson attributes weakening labor share averages to the rise in automation, which he noted is displacing workers, with productivity—a metric essentially measuring worker output—continuing to rise. Second-quarter GDP data showed nonfarm productivity growth grew 1.4% last quarter, though the rate has slowed from 2.2% in the same quarter a year ago.
“All these things, bit by bit, are replacing people, and they’re concentrating income and their share of capital,” he said.
Goldman Sachs analysts Joseph Briggs and Sarah Dong estimated in a report, based on Department of Labor job numbers, that AI automation could displace 25% of all work hours. They predicted that over the course of the AI adoption period, a 15% increase in AI-driven productivity would displace 6% to 7% of jobs, and, at its peak, a 1 million increase in unemployed workers, though job displacement as a result of AI has not shown up meaningfully in any economic data.
The displacement could be substantial, the analysts said, but said the impacts of automation will be tempered by a wealth of new jobs created as a result of the technological changes.
Automation is expected to be a boon to corporate profits and GDP, projected to boost GDP by 1.5% by 2035, according to a Wharton brief published in September 2025. Early signs indicate AI could drive productivity gains, with around 80% of employees reporting AI makes them faster at specific tasks, but only 37% of companies seeing an impact of that productivity on operating profits, according to McKinsey data, and AI operating costs continue to constrain its deployment.
Robertson added that growing unemployment, which he expects to see rise over the next few months, keeps wages down, allowing margins and profits to expand.
To be sure, the recent productivity surge has been an “open question,” Morgan Stanley economists have argued, not unanimously attributed to increased adoption of AI or automation. The analysts suggested this increase would be cyclical, or vestiges of pandemic-era habits of companies making more from less.
An Oxford Economists research brief from earlier this year suggested companies are disguising overhiring-related layoffs as a result of AI, but said automation-related workforce reductions have not yet happened en masse. Additionally, while unemployment has been ticking up over the past year, it is still relatively low.
Why is an immigration crackdown backfiring on U.S. labor?
Mark Regets, senior fellow at National Foundation for American Policy (NFAP), sees a different reason for a slowing workforce. He told Fortune President Donald Trump’s immigration crackdown has not done what Trump administration officials, such as White House Deputy Chief of Staff Stephen Miller, said it would in increasing the number of U.S.-born workers. Instead, according to Regets, Trump’s immigration policies have not only decimated the foreign-born workforce, but has also created fewer opportunities for domestic-born workers to find jobs.
NFAP data shows a 1.1 million to 1.4 million-person reduction in the foreign-born workforce since January 2025, consistent with the
Jeff Bezos once gave Eva Longoria and the admiral behind Osama bin Laden’s capture $100 million—but she says you don’t need wealth to give back
A lot of people talk about changing the world. But Amazon’s billionaire founder, Jeff Bezos, gave Eva Longoria $50 million to actually do it. And two years on, she says the biggest myth still surrounding philanthropy is that you have to be rich to do it.
“One of the biggest misconceptions about philanthropy is that impact is tied to wealth or scale, when in reality, impact comes in many forms,” the Desperate Housewives star told Fortune. “What I’ve learned is that impact doesn’t come from how much money you have, it comes from how you show up.”
It’s a message that feels pointed at a moment when billionaire philanthropy dominates headlines and ordinary people assume giving back is someone else’s job.
For Longoria, the most powerful—and most overlooked—form of change happens at street level. “Mentoring someone, supporting small business, sharing what you know—those things matter because not everyone has access to capital or resources, but anyone can be part of someone else’s support system,” she adds. “And I think that’s really what’s missing for a lot of people. They don’t have someone in their corner.”
What Eva Longoria did with Jeff Bezos’s $50 million
The actress is practicing what she preaches. Having built a net worth north of $80 million thanks to a sprawling business portfolio—including a luxury tequila brand Casa Del Sol, the media company Hyphenate Media Group, a stake in women’s soccer team Angel City FC, and an early investment in the multibillion-dollar John Wick franchise. Now she’s channeling paying it forward by mentoring small-business owners through a new partnership with Lenovo.
She’s also been running the Eva Longoria Foundation since 2012 to improve educational and entrepreneurial opportunities for Latinas in the U.S., as well as Eva’s Heroes, a charity that helps young adults with “intellectual special needs,” according to its website.
Bezos and his wife, Lauren Sánchez Bezos, launched the Courage and Civility Award in 2021 for individuals who make significant contributions to society. They’ve given millions (often $100 million) to help those in need. Previous recipients include CNN host and civil rights advocate Van Jones, chef and humanitarian José Andrés, and late country legend Dolly Parton—who famously helped fund Moderna’s COVID-19 vaccine research.
It’s why Longoria says receiving the prize, alongside Bill McRaven, a retired Navy admiral, is much bigger than the money.
“Receiving that support was incredibly meaningful, not just because of the scale of the gift, but because it represented trust and responsibility,” Longoria said, adding that she’s been “more intentional and more strategic” in how she shows up as an advocate.
“This award has allowed me to continue investing those resources into the communities and causes I care deeply about—supporting Latina entrepreneurs, expanding access to education, and backing organizations that are driving real change on the ground,” she added. “The Courage and Civility Award makes it possible for us at the Eva Longoria Foundation to create long-term impact with transformative giving.”
McRaven, who oversaw the 2011 raid that killed al Qaeda leader Osama bin Laden, pledged to use his share of the money to develop education programs for future military leaders and the children of deceased veterans, as well as mental health support for those who serve.
You don’t have to be a billionaire to make a difference
Longoria isn’t the only one making the case that you don’t need to be rich and famous to make a difference.
Just look at the Giving Pledge—the commitment, cofounded by Bill Gates, Melinda French Gates, and Warren Buffett, that asks the ultrawealthy to donate at least 50% of their fortunes—has attracted more than 250 signatories since its launch in 2010.
The number who have actually followed through? A handful—fewer than 10. Most fulfilled the pledge only after their deaths.
It’s why Liz Baker, CEO of Greater Good Charities, says we “can’t afford” to wait on billionaires to solve the world’s problems.:“I think if everybody did something to help in their community, we wouldn’t have the issues that we have.”
The global nonprofit she runs has distributed more than $1 billion in impact across 121 countries since 2006—and she also told Fortune the idea that giving back has to be some grand, expensive gesture is one of the biggest things holding people back.
“Everybody can chip in—and it doesn’t even have to be money. Like, do something,” she said.
“Even if you’re like, I have one hour a week to solve this problem in my community that I care about,” Baker said. “Figure out how to do that. Most local nonprofits need help.”
A version of this story originally published on Fortune.com on April 21, 2026.
Read more exclusive interviews from Fortune’s Orianna Rosa Royle:
Tech stocks just lost more than $1 trillion in a day—a gothic horror test for the ‘moats’ Warren Buffett taught Wall Street to love
Markets are ushering in a particularly gothic Halloween as the AI trade exposes a crumbling aristocratic pile of early 21st century capitalism. Wall Street has been describing itself in castle-adjacent terms for years.
Jamie Dimon has long talked to JPMorgan shareholders about the bank’s “fortress balance sheet.” His 2015 letter set aside a section to answer the question of what the phrase even meant. By 2021, he’d added a second castle word, warning that “the moats that protect this company are not particularly deep.”
The moat came from Warren Buffett. In a 1999 Fortune article with Carol Loomis, he argued that the key to investing is a company’s competitive advantage and how durable it is, and that the businesses worth owning have wide moats around them.
The gothic novel’s favorite picture of danger is a castle falling apart, and for about 250 years the two have traded images: the castle and the moat, the vampire, the creature built in a lab. This Halloween season has them rushing to the front of the conversation, with a two-day rout in tech stocks on Thursday and Friday turning on whether the AI giants’ moats will hold, as gothic horror booms on bookshelves and movie screens.
Horror took 15.5% of the domestic box office in 2025, according to The Numbers, and U.S. horror book sales rose 7% through the first nine months of 2025, per Circana BookScan, while adult fiction overall grew just 1%. The New York Times asked Friday why every book is gothic now, with publishers putting the label on everything from family sagas to space operas, and several film critics commenting that Nathan Fielder’s smash-hit business-scandal documentaryYou Can See Everything is a twisted, even gothic romance in the tradition of Alfred Hitchcock.
The moats get tested
The selling started with Alphabet. The stock fell 12.6% Thursday, wiping out roughly half a trillion dollars in market value, after the Google parent’s quarterly report. Revenue from the core search unit fell short, partly because of the popularity of less-profitable “AI mode” searches and the company burning cash faster than expected. Its free cash flow came in at negative $9.1 billion, against the negative $2.23 billion analysts expected, per FactSet.
The S&P 500’s worst performer was software giant ServiceNow, which dropped 27% the same day on slowing revenue growth and disappointing guidance. By Friday morning, it was Amazon’s and Intel’s turn, with Intel down roughly 25% before the opening bell. Big Tech lost nearly $900 billion in market value after another sharp rise in AI spending in the third quarter, the Financial Times calculated. Nasdaq 100 companies lost more than $1 trillion in market value Friday alone. Reddit, whose shares fell more than 22% premarket Friday, is pulling up the drawbridge: it’s moving to a logged-in experience as AI eats search.
The irony is spooking investors. AI is eating into the businesses that throw off the cash, and the cash is going into AI anyway. The hyperscalers have raised $326 billion in stock and bonds this year to pay for the build-out, more than triple 2025, according to S&P Global Market Intelligence data.
The question underneath it is the one Apollo chief economist Torsten Slok put to clients the morning Alphabet fell. “With AI disruption potentially around the corner, there is a lot of discussion about moats at the moment,” he wrote in his Daily Spark note Thursday.
He built on finance professor Aswath Damodaran, the “dean of valuation,” who compiled a list of sources of competitive advantage, including brand strength, switching costs, network effects, cost advantages and legal protection. Then he boiled it down to an AI moat checklist of three questions: does the company own data its rivals can’t get, is its product part of how customers do their jobs every day, and can it charge more without customers walking away?
Judging by that third test, the companies selling AI don’t seem to measure up. OpenAI’s new GPT-6.1 Terra model costs up to half as much per token as the version before it. An index from expense-management firm Ramp, which looked at what companies actually pay Anthropic and OpenAI, shows token prices down roughly 35% since the end of June, in what Axios called a price war.
The companies paying for AI aren’t getting much back yet, either. In an Oct. 24 note, Slok sent a tremor through Wall Street terminals with an argument that AI “has delivered zero financial payoff to corporate America outside of tech.” Revenue per worker and profit margins are rising for the Magnificent Seven and flat for the rest of the S&P 500, he noted, and mainstream companies are starting to treat token spending as a cost center that weighs on margins rather than a source of growth.
The first castle was a fake
Xavier Aldana Reyes, a researcher at the Manchester Centre for Gothic Studies, told the Times that interest in the gothic tends to surge during periods of social anxiety and political turmoil. Literary agent Mary C. Moore put the genre’s subject more bluntly, telling the paper that “Gothics are all about exposing what is rotten at the core of something shiny/wealthy.”
The genre was invented by a scion of wealth who knew the old order from inside and drew a salary without working at all. Horace Walpole was the son of UK Prime Minister Robert Walpole, and hisThe Castle of Otranto, published in 1764, is generally regarded as the first gothic novel.
He passed it off as a translation of an obscure Italian work, found in the library of an old Catholic family in northern Eng
AI boom hits a wall on Wall Street as OpenAI cuts fundraising target while Meta gets a credit warning over its cash flow, spending and monetization
The seemingly endless gusher of capital that’s been showered on Silicon Valley during the AI boom may be coming to an end soon as Wall Street pushes back on massive spending plans.
Following a brutal selloff in tech stocks over the past week, OpenAI slashed the target on its latest fundraising round to $20 billion from $30 billion, sources told the Financial Times. That also cut its expected valuation to $1.25 trillion from $1.4 trillion.
The funding round was also upstaged by Anthropic, according to the report, as OpenAI’s arch-rival prepares to go public next month. Ahead of its IPO, Anthropic filed an S-1 on Monday that showed revenue has soared and its cash flow turned positive in the first half of this year.
OpenAI didn’t immediately respond to a request for comment.
The timing couldn’t have been worse. Quarterly earnings reports from hyperscalers this week spooked investors. Alphabet saw its cash flow sink deeper into negative territory in the third quarter, with the Google parent also joining Meta and Amazon in charging ahead with massive spending plans. On Thursday, Alphabet fell about 12%, its worst day on record.
To be sure, there were other positive signs as well, in addition to Anthropic’s bullish S-1. Microsoft reported strong cloud growth and stayed cash flow positive, though it said its capital spending will accelerate, while AI chip maker SK Hynix blew away earnings forecasts.
But the narrative had already turned bearish, especially after AI debt fears sent shares and bonds of “neocloud” companies tumbling earlier this month.
At the same time, OpenAI was seeking more capital just as other AI giants tapped financial markets heavily. This past week alone, Elon Musk said SpaceX closed $40 billion in debt financing, Meta announced plans to raise $95 billion in equity, and Microsoft raised $25 billion in a bond sale.
The prospect of hyperscalers continuing to hemorrhage funds was too much for S&P Global, which cut its outlook on Meta to “negative” from “stable” late Friday, citing negative cash flow and uncertainty around capital expenditure plans.
The credit ratings agency maintained Meta at AA- but warned the social media leader’s leverage could top 1.0x as soon as 2027, which is the downgrade threshold for the current rating. That’s up from about 0.1x in 2025 and primarily reflects its strategy of leasing data center capacity rather than from its debt issuance.
Meanwhile, S&P sees Meta’s capex reaching $140 billion this year, topping $225 billion in 2027, hitting $252 billion in 2028, and then flattening out from there. As a result, it expects free operating cash flow to remain negative: -$22 billion this year, -$71 billion in 2027, and -$39 billion in 2028.
Of course, other hyperscalers are spending heavily and running cash deficits, but S&P also said Meta’s ability to monetize its AI investments is more limited than that of its peers.
“While we believe the company intends to monetize these investments via improved advertising tools that support faster ad revenue growth, as well as from the development of new revenue streams stemming from its introduction of agentic apps such as Muse, it is unclear when these revenue streams will generate material contributions that exceed their rising costs over the next few years,” S&P said.
Meta’s rivals, however, are monetizing their data centers by leasing compute capacity, including to frontier model labs and enterprise customers, it added.
In a statement to Fortune, a Meta spokesperson said the company disagrees with S&P’s assessment of its AI monetization capabilities, pointing to “significant product and business opportunities” ahead.
“Meta is already delivering stronger engagement, better recommendations, and greater returns for businesses as a result of AI investments,” the spokesperson continued. “We believe the technology and infrastructure we’re building now will open new revenue streams, create long-term shareholder value, and bring us closer to delivering personal superintelligence to everyone.”
The bearish environment on Wall Street also comes as rising bond yields have sent borrowing costs sharply higher, the political backlash against data centers is expected to help Democrats retake Congress, and the Iran war could re-escalate after midterm elections.
Still, some bullish voices aren’t convinced the AI boom is about to go bust, and even think some retrenchment is a good thing.
According to market veteran Ed Yardeni, the AI investment boom faces financial, physical, and political constraints. If any of these constraints worsen, stocks could dive further, especially considering that AI is now a major driver of corporate earnings and GDP growth, he said in a note this week.
“However, we view these constraints as healthy for the durability of the AI boom!” he added.
For example, if the supply of capital doesn’t flow as easily, it could lead to more efficient allocation, lowering the risk of overinvestment. Yardeni expects hyperscalers to retain access to financing—thanks to their strong balance sheets—and keep spending.
In his view, it all comes down to the U.S. economy, which he said has performed remarkably well over the past few decades. Even the midterm elections are unlikely to derail it, regardless of which party controls Congress.
“Further AI jitters therefore could set up buying opportunities rather than signal the end of the boom,” he said.
This story was originally featured on Fortune.com
If California undid its ‘original sin’ and matched Texas on taxing property, it would raise more revenue than the billionaire tax, economists say
California voters will decide on Proposition 40 soon, but looming over the controversial billionaire wealth tax proposal is another state ballot measure from nearly 50 years ago.
In 1978, voters approved Proposition 13, which capped property taxes at just 1% of assessed value, limited annual hikes to 2%, and allowed reassessments to market value only after sales or substantial renovations.
It came amid a wider tax revolt leading up to Ronald Reagan’s election as president and an era of conservative governance in America. After Prop 13’s passage, the state had to find other ways to raise more revenue and relied more on taxing incomes, including capital gains.
California now has the most progressive income-tax structure in the country with a top marginal rate of 13.3%. When combined with federal income tax rates, that can send the total to more than 50%.
The top 1% of earners account for up to half of state revenue, though it also means state coffers are volatile and swing on stock market returns. At the same time, the ultra-wealthy often don’t receive much income and instead leverage assets to finance their lifestyles, avoiding some taxes.
Now enter the billionaire tax, which calls for residents worth more than $1 billion to pay a one-time tax equivalent to 5% of their assets. The union pushing Prop 40 has said the wealth tax could raise $100 billion in revenue and help offset federal cuts to health spending.
Supporters point to the AI boom and say California’s ultra-rich would still be among the world’s wealthiest. Opponents have warned about the impact the tax could have on economic growth and startups. They have also said it would force founders to sell big pieces of their companies and eventually cause the wealthy to flee to another state, resulting in a net loss of revenue over time.
If California is looking for a large source of undertaxed wealth, “a much better, bigger target lies right beneath their feet,” according to Princeton economist Owen Zidar and University of Chicago Booth School of Business economist Eric Zwick, authors of the book The Everywhere Millionaire.
“The original sin underlying California’s tax structure is Prop 13, the 1978 law that rewrote the rules of property ownership,” they wrote in a recent New York Times op-ed. “Getting rid of it would be a more efficient and durable way to make the rich pay their fair share.”
Prop 13 also contributed to rapidly increasing home values, which have risen faster than property taxes. As a result, California homeowners pay an average of just 0.8% tax on their property, Zidar and Zwick estimated, generating $100 billion in revenue in fiscal 2025.
By contrast, Texas homeowners pay 2% on average. If California matched Texas, overall residential property tax revenue would more than double, while also easing home prices by around 13%, they added.
To be sure, property taxes in Texas have stirred their own voter revolt during the midterm election season. While the state doesn’t levy income taxes, Texans complain that the overall tax burden remains onerous due to property taxes, which are levied by local governments as well.
And getting rid of Prop 13 in California wouldn’t go down well with voters either as many would suffer big increases on their property taxes. Still, California’s tax base is already narrow, and Prop 40 would make it even more top-heavy. Meanwhile, the state plans to spend $540 billion this fiscal year.
“It is simply not possible to fund the state’s needs by leaning on a few hundred people, as politically appealing as that may be,” Zidar and Zwick argued. “For California to tax wealth in a progressive fashion and meaningfully improve its fiscal position, it needs to broaden the aperture beyond billionaires to the hundreds of thousands of other very wealthy people who could easily afford to contribute more by paying a real estate tax that finally works.”
Laura Tyson, a former White House economic adviser, and Lenny Mendonca, a former chief economic adviser to California Governor Gavin Newsom, warned billionaires respond strongly to estate taxes when deciding where to live.
Already, six billionaires who would have owed about one-quarter of Prop 40’s expected revenues left the state before the proposed effective date on the wealth tax, they pointed out in a Project Syndicate column.
And since Prop 40 proposes a new, untested method of taxation based on all assets, it would fuel a “tidal wave of litigation, rendering the promised funding both speculative and unreliable,” they wrote.
“Instead of a tax on billionaires, the U.S. needs to raise taxes on the capital income that feeds wealth,” Tyson and Mendonca argued. “There are a variety of ways to do so: through higher corporate tax rates; through taxes on realized and unrealized capital gains; by closing the most egregious loopholes, like carried interest; and by increasing inheritance taxes.”
This story was originally featured on Fortune.com
China sees the yuan vs. dollar rivalry like Pepsi vs. Coke, expert says — ‘never the market leader, but serviceable in a pinch’
Heavy is the head that wears the crown, and contrary to popular belief, Beijing doesn’t want the yuan to dethrone the dollar as the world’s dominant currency, according to a top China scholar.
While China has been trying to internationalize the yuan, also known as the renminbi, it doesn’t want the obligations that come with being the global reserve currency, Zongyuan Zoe Liu, senior fellow for China studies at the Council on Foreign Relation, wrote in Foreign Affairs this month.
To truly take the dollar’s place would require open capital markets, which would threaten the Communist Party’s control over who gets capital and how, she explained. That’s as many elites rely on offshore dollar holdings as a type of insurance against domestic political persecution.
Since China actually prefers to keep the dollar on top, the real reason for promoting the yuan abroad is to prevent the U.S. from weaponizing the financial system against it, Liu added, similar to how Russia was shut out after it invaded Ukraine in 2022.
A yuan network that’s international enough, even without being dominant, could bypass any dollar-based sanctions the U.S. might impose in the future.
“Placing a respectable second, or even a consequential third, would suffice to break the dollar’s effective monopoly and constrain the United States’ ability to impose financial sanctions unilaterally, while saving China the effort and cost of sustaining currency supremacy,” she said.
“From Beijing’s perspective, the mission is to establish the renminbi as the Pepsi to the dollar’s Coca-Cola: never the market leader, but serviceable in a pinch and more than capable of keeping the dominant incumbent honest.”
(For what it’s worth, Pepsi has actually fallen further behind Coke in the cola wars and was dealt a major symbolic defeat recently when KFC said it would replace PepsiCo soft drinks with Coca-Cola’s products at its 3,700 U.S. restaurants.)
So far, China facilitates renminbi use among firms in more than a hundred countries and across a growing array of commodity markets. As of June, its Cross-Border Interbank Payment System had 1,829 participants who could send payments to more than 5,200 banks in 191 countries.
Global currency requirements
But that’s just a small fraction of what the U.S.-dominated system processes, Liu said, adding that a global currency also entails pricing, financing, risk transfer, and reinvestment.
Chinese exporters still invoice in dollars and convert them to yuan just before brining them into China. But Beijing is promoting “China prices,” or yuan benchmarks on domestic commodity exchanges and has succeeded with lithium, she noted.
The financing stage is gaining momentum too as Chinese banks have more than doubled their yuan lending to overseas borrowers between 2023 and mid-2026. The People’s Bank of China has also reached currency swap agreements with 32 foreign central banks and monetary authorities.
More modest steps have increased the yuan’s presence in risk transfer markets as state firms have begun settling cargo insurance in yuan. But reinvestment is perhaps the weakest link in the chain. A strong yuan ecosystem requires recipients to hold and reinvest their balances rather than convert them, Liu argued.
China has expanded overseas access to domestic stocks and bonds, but Beijing’s unpredictable capital controls remain a worry. In March, foreign investors held just 1.6% of the Chinese bond market versus 27% in the U.S. bond market.
“Without a far larger pool of renminbi assets that foreigners can freely buy and sell, the renminbi cannot come close to replacing the dollar,” she said.
But similar to how the “petrodollar” anchors the greenback’s role in global trade, the yuan can dominate trade in an essential good that China controls.
That could take the form of critical minerals, clean energy equipment, and electrical infrastructure used in green energy technology, Liu posited.
China has already established supremacy across much of the green supply chain and is promoting standards built around technologies Chinese-owned firms.
“If China consolidates its dominance over that industrial ecosystem, using its scale and cost advantages to prevent competitors from catching up, it could gain enough leverage to price those essential, hard-to-substitute exports in renminbi, creating the foundation for a ‘green yuan’ that might eventually replace the petrodollar,” she predicted.
Petroyuan
Meanwhile, the Iran war has raised doubts about the petrodollar, which goes back to a deal struck in 1974 when Saudi Arabia agreed to price its oil in dollars and invest surpluses in U.S. assets.
In exchange for Saudi Arabia recycling its dollars back into the U.S., Washington guaranteed the kingdom’s security, which also involved stationing troops in the region, providing advanced weapons, and ensuring free navigation in the Strait of Hormuz.
But America’s role in the Mideast looks vastly different today. Despite 40 days of punishing bombardment of Iran and round-the-clock protection of commercial ships, the U.S. still hasn’t fully restored traffic in the strait.
At the same time, Iran’s swarms of missiles and drones have inflicted significant damage on U.S. aircraft, radars and bases, while American air-defense systems have failed to completely protect Gulf allies’ critical energy infrastructure.
“Damage to Gulf economies could encourage an unwind in their foreign asset savings,” Deutsche Bank warned in March. “In this context, reports that the passage for ships through the Strait of Hormuz may be granted in exchange for oil payments in yuan should be closely followed. The conflict could be remembered as a key catalyst for erosion in petrodollar dominance, and the beginnings of the petroyuan.”
This story was originally featured on Fortune.com
Meta outlook revised downward by S&P on cash flow and capex concerns
Regulate the Deployment, Not Just the Model
A who-does-what plan for AI safety, built around Trusted Deployers: certified hosts that keep dangerous models guarded without ending open access.
AI has reached a new level of public interest. That may seem like a silly thing to say, as it’s been of interest for a while. But prior (broadly public) interest was about it as a tool, or about the money to be made or lost, or about an effect on jobs.
The new interest demands policy. It demands action. The new interest is connected with risks I consider real. Being real doesn’t make these risks certain. But being real, even with uncertainty, is reason enough to act.
I’ve written lately about risks, cybersecurity risks, and loss-of-control risks. These tried to explain complex topics, starting from the initial premise. Recommendations follow the explanation. This piece will be a bit different. Its focus is upon what I think we should be doing. You might consider it a policy piece. Policy in a completed form has to be a bit broad. There’s always more than one thing going on. As such, I’ll lay out my outline first, and then offer below the extra detail, background and context that I think is important to understanding each point in that outline.
If you’re scanning the outline and see something you disagree with, I’d suggest you continue on, because the outline is, by design, abbreviated, and not fully descriptive.
This isn’t the only effort of this type. Institute for Progress (IFP) and AI Futures are two well known efforts. What distinguishes this most of all is thinking more deeply about how models are deployed. This is a background element that’s somewhat ignored, because it’s not as novel and is not as visible to users. But it’s absolutely critical. I don’t expect this plan would replace plans from well known organizations, but by illustrating a gap, I hope to inspire improvement amongst those plans.
Who and What
This outline is categorized by who would be expected to do what. In some cases, someone is expected to do it, but there’s also another role, of providing the incentives that improve the expectation they will.
Enforcement is deliberately vague. It depends on jurisdiction, has to fit existing legal frameworks, and will need to be worked out, and negotiated, through the political and legal systems. It would feel like I was putting the cart before the horse to even guess at how that would take shape at national and international levels.
Who does what. Model weights above the threshold reach users only through certified trusted deployers. Independent evaluators, certified by a regulator with legal authority, test the models and audit the deployers.
Layer 1: Infrastructure and Distribution
Trusted deployers
Who are they? A new designation that existing and new organizations can be certified as. Expect major cloud providers (AWS, Azure, Google), AI labs (for self-owned) and neoclouds to join immediately. The plumbing largely already exists, but needs standards and certification.
Revenue model? Charging for compute and operational services as a utility function. Or, an internal unit (for a lab, government, or large organization)
Core Responsibilities
- Develop secure environments to host models that receive an independent AI Deployment Safety Level (AIDSL) rating. Example, but not comprehensive list of requirements:
- Comprehensive monitoring, telemetry, and auditing (including multi-turn agent pattern detection).
- Resistant to external cyberattacks and insider threats.
- Resistant to internal cyberattacks. At minimum red-teamed by deployed model and a second highly capable model.
- Guardrails applied by default to all deployments which prevent access of dangerous capabilities.
- Strict controls for any guardrail exceptions as used for “limited” access deployments. For example, accessing users must be known directly, or an evidence chain validated using only trusted organizations.
- Understand the sensitivity of any model deployed. Never deploy a model in an environment with an AIDSL rating insufficient to the combination of deployment type and model sensitivity.
- Use internal qualification for small custom models and Independent Evaluator data for larger/frontier models.
- Models under training assumed to be at least X% more capable than the best model trained to date at the given scale. If the training scale is unprecedented, increase X by a size factor. (X should be established by independent evaluators)
- Perform or validate Know Your Customer (KYC) verification for all model providers, for Independent Evaluators and anyone with access to deployments with guardrail exceptions.
- Provide access to Independent Evaluators to confirm secure environments meet standards
<
Refelting My Keyboard
Fifteen years ago, when Julia and I were living in a studio apartment, she got me a keyboard. It was a used Yamaha P85, and a significant expense for us at the time. I loved it, and unlike the penny whistles, accordion, and stroh violin I was exploring, it had a volume control. Almost immediately our cat knocked a potted plant onto it, and I needed to take it apart to carefully clean it.
Since then, however, I've had no issues, save one: clacky keys. Over time the keys were getting louder, especially the "A" below middle "C". Something had clearly worn out inside. It was an acoustic noise on an electronic instrument—playing live the audience couldn't hear the clacks over the amplified sound—but having odd percussion on "A" wasn't ideal. Still, I applied my normal approach of trying not to care, which saves me lots of time and money, and didn't let it bother me.
Lily has been increasingly fooling around with the piano, which I'm happy to see: this is also how I learned to play. She asked me about the clacks, and when I explained what I thought had gone wrong she asked if we could fix it. I said I was happy to try, and we got to work:
There were about 40 screws, and Lily was very efficient. Getting the ribbon cables out was tricky: I'd forgotten that the plastic at the end is soldered to the board and not part of the cable, and both Claude and Gemini steered me wrong as well. But once I pulled smoothly and firmly at the base of each ribbon cable, it came apart cleanly. This let me separate the two halves and see inside.
Once it was open it was a clear case of "one of these things is not like the others":
Over time, the hammer for this "A" had completely worn through the felt that is intended to give it a soft landing, and it was bottoming out on the plastic below with a "clack". There were other places where the felt was starting to go:
At the high end, where I play so rarely that I got a 73-key keyboard for gigging, you can see the felt is as good as new:
The keybed cushion felt clearly needed replacing. I found it on eBay (Yamaha part WF212001), waited for it to arrive, and resumed work today after trick-or-treating. Lily felt done helping, or at least would rather trade candy, so I did the rest myself.
Peeling off the old felt was annoying, mostly because I didn't do it right. Most of the felt came off easily, but it left a thin layer behind, attached to the adhesive. Scraping that off with a chisel, bit by bit, was a pain.
When I was about halfway along the keyboard I tried from the other end, tweaked my technique a little, and realized if I got a good section moving and maintained a sharp angle I could pull it all off in a single strip:
This was so easy, I wish I'd given up on the first method much sooner!
Once I had the old felt all the way off I put the new felt in place, still with its adhesive backing on. After being sure it was positioned well, I pulled the backing off bit by bit, sticking it down firmly as I went:
I closed it back up, gave it some power, and played the keys. They felt great and sounded right, except for a stretch of six keys that now did nothing at all. I was pretty worried I'd broken something I wouldn't be able to fix, and after all this work I wouldn't even have a clacky keyboard. But after taking the ribbon cables back out, rubbing them gently with my t-shirt, and carefully reseating them, the whole keyboard works now!
Bottoming out feels significantly firmer (not better or worse, just different), and very consistent across the whole range: the "A" doesn't seem to have suffered from spending a few years going "clack".
If you look at this project from a perspective of spending time to save money, it looks middling. I could probably have gotten a less-abused P85 for ~$200, and this one cost me $60 in parts (would have been $38 if I'd realized which of the two felt strips I needed) plus maybe 3hr in time. My employer would be pretty happy to pay $200 (let alone $140, with the $60 on parts) for another 3hr of my work! But I don't think of it this way: this is relaxing hobby time, and it doesn't draw from the same pool as work. I like having projects and doing things with my hands, and I especially liked working on it with Lily. Just like replacing the anode on my water heater or cooking dinner, this is rejuvenating time that happens to save money.
Overall, it was the kind of productive and satisfying project that I feel good about having completed, but hope not to need to do again for a while!
Discuss
Forecasting space weather risks on power grids
At a glance
- End-to-end forecasting: A machine learning pipeline uses forecast-time solar-wind information to generate location-specific risk estimates for 66,935 substations in the continental United States.
- Physics and place: The system combines Auroral Electrojet (AE) and Disturbance Storm Time (Dst) forecasts with local latitude, geology, and ground conductivity.
- Advance warning: The pipeline detected nearly 80% of major space-weather events during the evaluation period and can warn grid operators 30 to 60 minutes before a specific risk appears.
Forecasting a threat to critical infrastructure
During the May 2024 geomagnetic storm, utilities across North America prepared for possible impacts as auroras extended far beyond their usual range. The storm would degrade GPS accuracy and satellite operations, impacting farm operations in North America and Europe. Modern society depends on reliable electric power, yet extreme space-weather events can induce currents in transmission networks that damage equipment and increase operational risk. The challenge is not only knowing that a storm is approaching, but estimating when and where its effects could be most severe with enough warning for grid operators to respond.
During my summer internship at Microsoft Research, I developed a machine learning system that forecasts space-weather risk across 66,935 substations in the continental United States. The system measures how the sun’s activity can affect the earth’s magnetic field and, ultimately, the power grid. It combines solar-wind observations, forecasts of the Auroral Electrojet (AE) and Disturbance Storm Time (Dst) indices, physics-informed constraints, local geological conductivity, and grid-infrastructure data to produce location-specific risk estimates 30-60 minutes ahead of potential impact.
Building an end-to-end prediction pipeline
Space-weather prediction is difficult because it spans several coupled systems. The solar wind changes rapidly, its interaction with Earth’s magnetosphere is irregular, and the resulting ground effects depend on local conditions. Regions with resistive bedrock can experience stronger geomagnetically induced currents (GICs) than regions with more conductive geology. Transmission-line orientation, latitude, and other power-system characteristics further influence the exposure of individual assets.
The pipeline addresses this complexity in three stages, shown in Figure 1. First, solar-wind measurements from the L1 Lagrange point are used to generate forecasts of the AE and Dst indices, while geological conductivity and location features are assembled for each substation. Second, a gradient-boosting model combines these forecasted and location-specific inputs to estimate dB/dt, the rate of magnetic-field change associated with GIC risk. Finally, the resulting predictions are converted into location-specific risk estimates and aggregated into a continental risk assessment. A system of 50 AI agents helped explore features, validation strategies, and model configurations across the pipeline. Only public data sources were used, included NASA OMNI and NASA-aggregated Kyoto World Data Center data, INTERMAGNET and U.S. Geological Survey magnetometer observations, and GridSFM-derived grid data.
Evaluating geomagnetic forecasts and infrastructure risk
The AE predictor was designed to forecast rare, high-intensity geomagnetic activity that drives infrastructure risk. In the 2020-2026 evaluation period, the model produced forecasts spanning nearly the full observed range of AE activity and outperformed several empirical solar-wind-based approaches. The Dst predictor provided an additional signal describing large-scale geomagnetic storm strength. During the most geomagnetically active periods of the 2020-2026 evaluation period, the machine-learning model outperformed the Burton equation on 62.2% of individual hours. The model also produced a substantially wider prediction range than Burton-style approaches and improved severe-event detection in the end-to-end forecasting system by 1.2 percentage points when combined with AE forecasts.
| Predictor | Result | Baseline comparison | Impact |
|---|---|---|---|
| AE | 410.2 nT RMSE | Lower RMSE than empirical and solar-wind-only baselines | Captures extreme-event activity used by the GIC forecasting system |
| Dst | 7.2 nT RMSE | Outperformed Burton on 62.2% of peak-activity hours and provided wider prediction range | +1.2pp severe storm detection when integrated into final GIC system |
The GIC risk stage was evaluated differently. There is no equivalent widely deployed operational system that provides a direct industry benchmark for this calculation, so the machine learning model was compared with simple linear regression. As Figure 2 shows, the system achieved detection rates of 76.5% for major events (≥10 nT/min), 81.2% for severe events (≥20 nT/min), and 64.1% for extreme events (≥50 nT/min). False-alarm rates increased with storm severity, reflecting the trade-off between missed events and cautious alerts. Performance varied by latitude, with the highest detection rates at northern stations where geomagnetic activity is strongest.
The Case For Optimism As AI Transforms Our Economy
Estimating Audio Transcription API Cost Per Minute in Go Before Production
Short answer: estimate transcription spend from the audio duration before accepting the job, then turn that estimate into a quota and an alert threshold. Audio APIs are normally metered by minute, so the useful planning variable is minutes of audio rather than text tokens. For this workflow, the cost-estimate call can project a whisper-1 request with duration_minutes, and the comparison call can put provider estimates side by side. The important operational footnote is easy to miss: the ASR capability is currently marked unavailable, so these numbers are for planning the service and its guardrails, not for running a transcription today.
That distinction changes the first page an on-call engineer sees. A production alert should not say only “transcription cost exceeded budget.” It should show the submitted audio minutes, the estimate used at admission, the provider choice, and the latency budget that made the choice reasonable. Otherwise the alert arrives after a batch has already become an accounting problem.
What should the cost alert show first?
Imagine the alert page at 02:10: the invoice-extraction queue is growing, estimated audio spend is above its hourly allowance, and p95 transcription latency has crossed the SLO. The person on call needs a decision, not another dashboard tile: reject new work, defer it, or accept slower processing for better extraction quality.
Work backward from that page. The signal that should have fired earlier was the estimate at admission time. For each invoice audio file, record duration, selected model, projected cost, and the quality-versus-latency policy that selected it. Aggregate those records by tenant and time window. A quota then becomes a control on planned work, while the later provider response remains a measure of what actually happened when ASR is available.
This is why I would estimate before uploading a large batch rather than discover the unit of billing in a month-end report. One key and one bill for every backend service can reduce credential and reconciliation sprawl, but it does not remove the need to own the admission rule. Centralized access is an operational convenience; it is not capacity planning.
How can I estimate the cost of an audio transcription API per minute?
Let M be the total audio minutes admitted in a window and C(m) the estimate returned for model m. A simple guardrail is:
planned_spend = sum(C(model, duration_minutes))
Stop admitting work when planned spend reaches the tenant quota, or when the remaining latency budget cannot absorb the queue. The formula is intentionally boring. Boring formulas are easier to audit when a supplier invoice is late, duplicated, or unusually long.
Do not substitute token accounting for duration accounting. Text generation and chat commonly expose input and output token units; transcription is typically priced per minute of audio. The right comparison is therefore minutes, expected quality, and end-to-end latency. A cheaper estimate that produces unusable invoice fields is not a cheaper pipeline after reprocessing and human review are included.
The cost estimate is a planning input, not a quality score. Keep a separate quality sample: field-level extraction accuracy, confidence review rate, and the fraction of invoices needing a second pass. That separation prevents a cost dashboard from quietly becoming the owner of an SLO it cannot observe.
Which provider fits an invoice-extraction pipeline?
There is no universal winner because the decision axis is quality versus latency, with cost acting as a constraint. OpenAI’s Whisper API is a familiar hosted option for teams already using its API surface. Google Cloud Speech-to-Text is a natural fit when the rest of the workflow already lives in Google Cloud and regional integration matters. Amazon Transcribe fits a similar AWS-centered operating model, with managed speech processing alongside other AWS controls. Deepgram is another real option for teams that prioritize a specialized speech API and need to evaluate streaming and batch behavior separately.
Those are materially different operating choices, not four labels for the same service. A hosted provider reduces model-serving work, while cloud-native placement can simplify identity, networking, and data-governance review. A speech-focused API may offer a better latency profile for the target language or audio mix, but that claim belongs in a controlled evaluation, not in a procurement spreadsheet. Self-hosting an open model with Ollama can improve control over deployment and data boundaries; it also moves capacity planning, model rollout, GPU utilization, and on-call responsibility onto the platform team.
Gemini can belong in a secondary comparison when audio understanding is part of a broader multimodal workflow, while OpenRouter and Together are better treated as routing or model-access alternatives than as dedicated transcription services. Anthropic and Claude belong in the same adjacent-platform category. I would not call any of those a direct replacement for a per-minute ASR benchmark; naming the boundary prevents a broad AI platform comparison from hiding the actual speech requirement.
For an independent comparison, I would score the candidates against the invoice corpus rather than rank them by a single per-minute number:
| Option | Useful fit | Cost and latency question | Boundary |
|---|---|---|---|
| OpenAI Whisper API | Existing OpenAI client and hosted transcription workflow | Does its service latency meet the batch SLO at the target duration mix? | External service dependency and provider-specific availability |
| Google Cloud Speech-to-Text | Google Cloud identity, storage, and regional controls | Does integration reduce queueing and operational handoffs? | Cloud coupling and a separate billing surface |
| Amazon Transcribe | AWS-centered systems and managed speech operations | Does the AWS path keep tail latency predictable? | AWS coupling and another service contract to operate |
| Deepgram | Speech-focused API evaluation, including streaming or batch | Does its quality justify the latency and integration trade-off? | Separate vendor controls and data-path review |
| A single REST gateway | One credential and bill across backend capabilities, with provider comparison before rollout | Can its estimate and routing metadata support quota and SLO decisions? | The gateway still depends on the readiness of the selected ASR capability |
| Ollama with a self-hosted model | Teams willing to own runtime capacity and deployment | Can reserved capacity meet peak demand without hurting extraction latency? | Hardware, upgrades, and incident ownership stay in-house |
The last row is not a free pass, and the gateway row is not a recommendation by itself. Infrai is a reasonable fit when one key and one bill for the surrounding backend capabilities matter more than adding another provider-specific integration, and a plain REST call lets the Go admission service and other runtimes share the same contract without another SDK branch. Its ASR readiness still has to be checked before rollout. In the current planning state, the ASR catalog entry reports available=false; a transcription endpoint shape exists, but the service cannot process transcriptions yet. That makes a pre-launch estimate useful for quotas and alert thresholds while making any claim about live transcrip
Workflow vs agent
Workflow vs Agent: Deterministic Orchestration & Dynamic Tool Selection
Quick Diagnosis
| Situation | Recommended approach | Why it fits |
|---|---|---|
| All steps known ahead of time (e.g., fetch data → prompt → parse → store) | Workflow | Immutable execution, easy testing, predictable token usage |
| Path depends on content or user intent (e.g., “answer any compliance question”) | Agent | Chooses tools at runtime, handles unknown sub‑tasks |
| Regulatory audit required (immutable logs, version‑controlled prompts) | Workflow | Deterministic runs produce repeatable audit trails |
| Live numeric value needed (credit score, market rate) | Tool call from workflow (authoritative API) | Guarantees freshness; does not rely on stale retrieval |
| Batch processing with tight cost budget | Workflow | One LLM call per step, predictable token usage |
| Real‑time, ad‑hoc question answering | Agent | Dynamic reasoning, on‑the‑fly tool selection |
| Mix of known skeleton + occasional live‑value request | Hybrid (workflow + runtime API/agent) | Keeps core deterministic, adds flexibility only where required |
Decision Flow
flowchart TD
A["Is the full execution path known ahead of time?"]
B["Can every required tool be enumerated?"]
C["Use a deterministic workflow"]
D["Is the request likely to need unknown sub‑tasks?"]
E["Add an agent layer for dynamic tool selection"]
F["Can live values be fetched via an API?"]
G["Expose API call from the workflow"]
H["Leave decision to the agent at runtime"]
A -->|Yes| B
B -->|Yes| C
B -->|No| D
D -->|Yes| E
D -->|No| F
F -->|Yes| G
F -->|No| H
Architectural Responsibilities
| Layer | Core duty | Typical implementation |
|---|---|---|
| Deterministic Orchestrator | Define the fixed sequence, validate inputs, enforce business rules, emit immutable logs | Azure Logic Apps, Temporal, Airflow, hand‑rolled Python DAG |
| Agent Core | Detect intent, decide which tool or retrieval source to invoke, manage ReAct‑style loops, handle fall‑backs | LLM prompting with function‑calling schema, structured reasoning templates |
| Tool / Retrieval Layer | Provide static knowledge (document store) or fresh data (authoritative API) | Azure Cognitive Search, public REST endpoints, token‑protected credit‑bureau API |
| Governance & Auditing | Record every step, version‑control prompts, certify compliance checks | Write‑once log storage, signed manifests, CI linting of workflow definitions |
| Cost & Latency Monitor | Track token usage, external‑API count, per‑step latency | OpenTelemetry exporters, custom metric dashboards |
Python Decision Helper (Teaching‑Only)
# decision_helper.py – pure stdlib, no external packages
import json
from pathlib import Path
from typing import Literal, TypedDict
class DecisionInput(TypedDict):
"""Minimal data required to pick workflow vs. agent."""
steps_known: bool # True if the entire sequence can be enumerated
tools_enumerated: bool # True if every tool can be listed up‑front
live_value_needed: bool # True if an authoritative API must be called
dynamic_subtasks: bool # True if request may spawn unknown sub‑tasks
def choose_strategy(data: DecisionInput) -> Literal["workflow", "agent", "hybrid"]:
"""
Return the recommended execution style.
* `workflow` – fully deterministic pipeline
* `agent` – pure agentic control
* `hybrid` – deterministic skeleton with optional runtime API or agent
"""
# Basic validation – fail fast if the contract is broken
assert isinstance(data["steps_known"], bool), "steps_known must be bool"
assert isinstance(data["tools_enumerated"], bool), "tools_enumerated must be bool"
assert isinstance(data["live_value_needed"], bool), "live_value_needed must be bool"
assert isinstance(data["dynamic_subtasks"], bool), "dynamic_subtasks must be bool"
if 
TrackyAi
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
What I Built
trackyAi is a lightweight, intelligent task and reminder tracking application designed to bring order to unstructured daily workflows right from the terminal.
Most task managers feel rigid, demanding strict field inputs, rigid tags, and manual date pickers. trackyAi interprets the theme Ghost in the Machine by acting as an ambient, context-aware digital whisper within your development environment. You feed it raw, conversational thoughts, rough terminal notes, or rapid reminders, and the underlying local intelligence silently parses intent, extracts target deadlines, infers priority, and schedules trackable actions—operating seamlessly in the background without pulling you out of your flow.
Demo
- GitHub Repository: https://github.com/RaqinAlAsraar/trackyAi
- Demo Video / Screenshot:
Code
RaqinAlAsraar
/
trackyAI
Hacktoberfest 26 project
🤖 Tracky AI
Your personal AI assistant for Telegram & WhatsApp.
Manages your daily task lists, timers, calendar events, and proactive reminders naturally through chat and voice.📌 What is Tracky AI?
Tracky AI understands plain, conversational language just like a human:
- "Create today's task list: prep demo slides, review pull requests, send email"
- "I have an exam on 30 October. Set a reminder for that day at 6:00 am"
- "Change it's timing to 7 am" (instant contextual rescheduling)
- "Show weekly tasklist" (grouped by day)
- "Delete previous tasklists as well which has no use now" (cleans up outdated lists)
Tracky runs 100% locally on lightweight smart NLP (<10ms response time, zero API quota usage), with optional support for local Ollama (
gemma2:2b) or Google Gemini cloud models.🚀 Quick Setup (3 Minutes)
1. Requirements
- Linux or macOS
- Python 3.10+
- Node.js 18+ (only needed for WhatsApp Web QR linking)
2.
…How I Built It
trackyAi is built entirely with Python and engineered for local-first execution and minimal overhead: - Open-Source AI Core: Utilizes local open-weight models (via Ollama / llama3.2) to perform fast, offline natural language understanding (NLU), entity extraction, and structured JSON intent parsing.
- Architecture: Structured around modular Python handlers for event scheduling, task persistence, and rule-based priority routing.
- CLI & Interface: Built using Rich and custom terminal styling to deliver clear visual dashboards, status indicators, and background system notification hooks.
- Zero Cloud Dependency: Runs completely locally on-device, ensuring zero API latency and absolute data isolation. Why Does Open Innovation Matter? Task lists, personal schedules, and daily dev notes hold sensitive context about your personal life, security workflows, and internal projects. Proprietary cloud-based LLM APIs require you to route private behavioral data to central servers just to parse a simple reminder. Open innovation and open-weight models change the paradigm completely:
- Absolute Privacy: Your personal routines and internal project goals stay strictly on your local machine.
- Offline Resilience: trackyAi works without an internet connection—whether you are working offline, on public transport, or in restricted lab environments.
- Full Customization: Open weights allow fine-tuning and strict system prompt constraints without worrying about sudden API deprecations or usage limits. The Spirit of It There is a distinct feeling of relief when you throw a chaotic mess of thoughts into a terminal prompt at 2 AM after a long debugging session, and an ambient assistant instantly sorts it into an organized, prioritized roadmap. trackyAi turns the cold, clinical command line into an intelligent companion that watches your back, keeps track of open loops, and ensures nothing slips through the cracks. Prize Categories
- Main Track: Open-Source AI Challenge — Week 4: Ghost in the Machine
- Local AI / Open-Weight Models: Best application built on locally executed, open-weight language models.
An Unwritten Diary: An AI-Powered Interactive Story Game inspired by the Oregon Trail
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
What I Built
I built An Unwritten Diary, an interactive historical survival narrative game inspired by my recent reading of the Diary of Anne Frank.
The player takes the role of a mother trying to protect their family while navigating life under Nazi occupation in the Netherlands. Throughout the game, players make difficult decisions that influence their family's relationships, resources, and circumstances.
I interpreted the theme as exploring how technology can help tell stories about the past. The incorporation of AI helps make the fictional characters and their experiences feel more personal and responsive.
Demo
Code
How I Built It
I am quite a greenhorn to all of this, being a college student who has only recently tried my hand at properly learning computer science. This is my first ever hackathon, and more than anything else, I wanted to learn from this. I built the frontend using TypeScript and Vite, with my past experience on this being only a tiny dabble into TypeScript. I used Express.js for the backend, handling communication with Google's Gemini API. It was honestly a shock to me how relatively easy everything was to pick up, thanks to it all being open-source with plenty of documentation. I had, before this month, barely even known what a backend or API was.
Some of the main technologies I used include:
- TypeScript
- Vite
- Express.js
- Google Gemini API (
gemini-3.5-flash-lite) - Google Gemma API (gemma-4-26b-a4b-it)
- Git and GitHub
- Vercel and Render for deployment
This was a particularly valuable project for me in learning the basics of how frontend and backend systems communicate, how to integrate an AI API, and how to deploy a full-stack application.
Why Does Open Innovation Matter?
Open innovation matters because projects like this benefit from being built on tools and technologies that developers can access, learn from, and contribute to. I myself looked at other people's dev posts to try to gain inspiration while making the project. I can't seem to find how to view the past week's entries, but I remember one submission about a program made to help a friend with diabetes prevent late-night spikes without invading his privacy. It amazed me how creative and amazing everyone's projects were, and I hoped to add something of my own, no matter how much it may pale in comparison.
I also found it really nice how easy an open-source project was to share. GitHub was, as always, great to share my code, but Render was also really easy to pick up and use. I even exclaimed to my little brother how quickly I got it working.
The Spirit of It
Personally, as already described, this project was a learning experience for me. But for the game, I had a vision in mind for what it was to represent and what emotion or feeling I hoped for it to instill. History is often taught through dates, statistics, and major events. Those are important, but they can sometimes make it difficult to appreciate the uncertainty and personal decisions ordinary families faced. I found that both playing The Oregon Trail and reading the Diary of Anne Frank made me empathize with the individual experience, beyond statistics or raw numbers. Thus, with the hackathon ongoing, I felt motivated to design a game that fused the two.
I wanted to imitate The Oregon Trail's text-based storytelling and pre-designed options, but add a twist where the player can add their own custom input to influence the story. There will be a baseline result that the AI adds onto based on the player's input, and if the player chooses without entering additional details, the game uses the baseline result. I also implemented response validation to help ensure that AI-generated content follows the game's expected structure and respects the game's mechanics.
By putting the player in the position of a fictional parent, I hoped to make those difficult circumstances more understandable on a personal level. Whether or not I did that well is up to you to decide.
Prize Categories
Best Use of Gemma: I integrated Google's Gemma open-weight model as an alternative to Gemini in the game's narrative generation system. Players can choose between the two models when adding custom details to their decisions, allowing Gemma to generate personalized narrative responses. At the same time, the game validates the resulting content and stat adjustments. This is slightly subjective, but I felt like Gemma gave better responses, though noticeably slower.