In July 4 ceremony, Trump signs tax and spending bill into law
With military jets flying overhead and hundreds of supporters in attendance, Trump signed the bill one day after the Republican-controlled House of Representatives narrowly approved the signature legislation of the president's second term.
The bill, which will fund Trump's immigration crackdown, make his 2017 tax cuts permanent, and is expected to knock millions of Americans off health insurance, was passed with a 218-214 vote after an emotional debate on the House floor.
"I've never seen people so happy in our country because of that, because so many different groups of people are being taken care of: the military, civilians of all types, jobs of all types," Trump said at the ceremony, thanking House Speaker Mike Johnson and Senate Majority Leader John Thune for leading the bill through the two houses of Congress.
"So you have the biggest tax cut, the biggest spending cut, the largest border security investment in American history," Trump said.
Trump scheduled the ceremony on the South Lawn of the White House for the July 4 Independence Day holiday, replete with a flyover by stealth bombers and fighter jets like those that took part in the recent U.S. strikes on nuclear facilities in Iran. Hundreds of Trump supporters attended, including White House aides, members of Congress, and military families.
After a speech that included boastful claims about the ascendance of America on his watch, Trump signed the bill, posed for pictures with Republican congressional leaders and members of his cabinet, and waded through the crowd of happy supporters.
The bill's passage amounts to a big win for Trump and his Republican allies, who have argued it will boost economic growth, while largely dismissing a nonpartisan analysis predicting it will add more than $3 trillion to the nation's $36.2 trillion debt.
While some lawmakers in Trump's party expressed concerns over the bill's price tag and its hit to healthcare programs, in the end just two of the House's 220 Republicans voted against it, joining all 212 Democrats in opposition.
The tense standoff over the bill included a record-long floor speech by House Democratic Leader Hakeem Jeffries, who spoke for eight hours and 46 minutes, blasting the bill as a giveaway to the wealthy that would strip low-income Americans of federally-backed health insurance and food aid benefits.
Democratic National Committee Chair Ken Martin predicted the law would cost Republicans votes in congressional elections in 2026.
"Today, Donald Trump sealed the fate of the Republican Party, cementing them as the party for billionaires and special interests - not working families," Martin said in a statement. "This legislation will hang around the necks of the GOP for years to come. This was a full betrayal of the American people. Today, we are putting Republicans on notice: you will lose your majority."
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Finextra
36 minutes ago
- Finextra
Context Engineering for Financial Services: By Steve Wilcockson
The hottest discussion in AI right now, at least the one not about Agentic AI, is about how "context engineering" is more important than prompt engineering, how you give AI the data and information it needs to make decisions, and it cannot (and must not) be a solely technical function. "'Context' is actually how your company operates; the ideal versions of your reports, documents & processes that the AI can use as a model; the tone & voice of your organization. It is a cross-functional problem.' So says renowned Tech Influencer and Associate Professor at Wharton School, Ethan Molick. He in turn cites fellow Tech Influencer Andrej Karpathy on X, who in turn cites Tobi Lutke, CEO of Shopify: "It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM. " The three together - Molick, Karpathy and Lutke - make for a powerful triumvirate of Tech-influencers. Karpathy consolidates the subject nicely. He emphasizes that in real-world, industrial-strength LLM applications, the challenge entails filling the model's context window with just the right mix of information. He thinks about context engineering as both a science—because it involves structured systems and system-level thinking, data pipelines, and optimization —and an art, because it requires intuition about how LLMs interpret and prioritize information. His analysis reflects two of my predictions for 2025 one highlighting the increasing impact of uncertainty and another a growing appreciation of knowledge. Tech mortals offered further useful comments on the threads, two of my favorites being: 'Owning knowledge no longer sets anyone apart; what matters is pattern literacy—the ability to frame a goal, spot exactly what you don't know, and pull in just the right strands of information while an AI loom weaves those strands into coherent solutions.' weaves those strands into coherent solutions.' 'It also feels like 'leadership' Tobi. How to give enough information, goal and then empower.' I love the AI loom analogy, in part because it corresponds with one of my favorite data descriptors, the "Contextual Fabric". I like the leadership positivity too, because the AI looms and contextual fabrics, are led by and empowered by humanity. Here's my spin, to take or leave. Knowledge, based on data, isn't singular, it's contingent, contextual. Knowledge and thus the contextual fabric of data on which it is embedded is ever changing, constantly shifting, dependent on situations and needs. I believe knowledge is shaped by who speaks, who listens, and what about. That is, to a large extent, led by power and the powerful. Whether in Latin, science, religious education, finance and now AI, what counts as 'truth' is often a function of who gets to tell the story. It's not just about what you know, but how, why, and where you know it, and who told you it. But of course it's not that simple; agency matters - the peasant can become an abbot, the council house schoolgirl can become a Nobel prize-winning scientist, a frontier barbarian can become a Roman emperor. For AI, truth to power is held by the big tech firms and grounded on bias, but on the other it's democratizing in that all of us and our experiences help train and ground AI, in theory at least. I digress. For AI-informed decision intelligence, context will likely be the new computation that makes GenAI tooling more useful than simply being an oft-hallucinating stochastic parrot, while enhancing traditional AI - predictive machine learning, for example - to be increasingly relevant and affordable for the enterprise. Context Engineering for FinTech Context engineering—the art of shaping the data, metadata, and relationships that feed AI—may become the most critical discipline in tech. This is like gold for those of us in the FinTech data engineering space, because we're the dudes helping you create your own context. I'll explore how five different contextual approaches, all representing data engineering-relevant vendors I have worked for —technical computing, vector-based, time-series, graph and geospatial platforms—can support context engineering. Parameterizing with Technical Computing Technical computing tools – think R, Julia, MATLAB and Python's SciPy stack - can integrate domain-specific data directly into the model's environment through structured inputs, simulations, and real-time sensor data, normally as vectors, tables or matrices. For example, in engineering or robotics applications, an AI model can be fed with contextual information such as system dynamics, environmental parameters, or control constraints. Thus the model can make decisions that are not just statistically sound but also physically meaningful within the modeled system. They can dynamically update the context window of an AI model, for example in scenarios like predictive maintenance or adaptive control, where AI must continuously adapt to new data. By embedding contextual cues, like historical trends, operational thresholds, or user-defined rules, such tools help ground the model's outputs in the specific realities of the task or domain. Financial Services Use Cases Quantitative Strategy Simulation Simulate trading strategies and feed results into an LLM for interpretation or optimization. Stress Testing Financial Models Run Monte Carlo simulations or scenario analyses and use the outputs to inform LLMs about potential systemic risks. Vectors and the Semantics of Similarity Vector embeddings are closely related to the linear algebra of technical computing, but they bring semantic context to the table. Typically stored in so-called vector databases, they encode meaning into high-dimensional space, allowing AI to retrieve through search not just exact matches, but conceptual neighbors. They thus allow for multiple stochastically arranged answers, not just one. Until recently, vector embeddings and vector databases have been primary providers of enterprise context to LLMs, shoehorning all types of data as searchable mathematical vectors. Their downside is their brute force and compute-intensive approach to storing and searching data. That said, they use similar transfer learning approaches – and deep neural nets – to those that drive LLMs. As expensive, powerful brute force vehicles of Retrieval-Augmented Generation (RAG), vector databases don't simply just store documents but understand them, and have an increasingly proven place for enabling LLMs to ground their outputs in relevant, contextualized knowledge. Financial Services Use Cases Customer Support Automation Retrieve similar past queries, regulatory documents, or product FAQs to inform LLM responses in real-time. Fraud Pattern Matching Embed transaction descriptions and retrieve similar fraud cases to help the model assess risk or flag suspicious behavior. Time-Series, Temporal and Streaming Context Time-series database and analytics providers, and in-memory and columnar databases that can organize their data structures by time, specialize in knowing about the when. They can ensure temporal context—the heartbeat of many use cases in financial markets as well as IoT, and edge computing- grounds AI at the right time with time-denominated sequential accuracy. Streaming systems, like Kafka, Flink, et al can also facilitate the real-time central nervous systems of financial event-based systems. It's not just about having access to time-stamped data, but analyzing it in motion, enabling AI to detect patterns, anomalies, and causality, as close as possible to real time. In context engineering, this is gold. Whether it's fraud that happens in milliseconds or sensor data populating insurance telematics, temporal granularity can be the difference between insight and noise, with context stored and delivered by what some might see as a data timehouse. Financial Services Use Cases Market Anomaly Detection Injecting real-time price, volume, and volatility data into an LLM's context allows it to detect and explain unusual market behavior. High-Frequency Trading Insights Feed LLMs with microsecond-level trade data to analyze execution quality or latency arbitrage. Graphs That Know Who's Who Graph and relationship-focussed providers play a powerful role in context engineering by structuring and surfacing relationships between entities that are otherwise hidden in raw data. In the context of large language models (LLMs), graph platforms can dynamically populate the model's context window with relevant, interconnected knowledge—such as relationships between people, organizations, events, or transactions. They enable the model to reason more effectively, disambiguate entities, and generate responses that are grounded in a rich, structured understanding of the domain. Graphs can act as a contextual memory layer through GraphRAG and Contextual RAG, ensuring that the LLM operates with awareness of the most relevant and trustworthy information. For example, graph databases - or other environments, e.g. Spark, that can store graph data types as accessible files, e.g. Parquet, HDFS - can be used to retrieve a subgraph of relevant nodes and edges based on a user query, which can then be serialized into natural language or structured prompts for the LLM. Platforms that focus graph context around entity resolution and contextual decision intelligence can enrich the model's context with high-confidence, real-world connections—especially useful in domains like fraud detection, anti-money laundering, or customer intelligence. Think of them as like Shakespeare's Comedy of Errors meets Netflix's Department Q. Two Antipholuses and two Dromios rather than 1 of each in Comedy of Errors? Only 1 Jennings brother to investigate in Department Q's case, and where does Kelly MacDonald fit into anything? Entity resolution and graph context can help resolve and connect them in a way that more standard data repositories and analytics tools struggle with. LLMs cannot function without correct and contingent knowledge of people, places, things and the relationships between them, though to be sure many types of AI can also help discover the connections and resolve entities in the first place. Financial Services Use Cases AML and KYC Investigations Surface hidden connections between accounts, transactions, and entities to inform LLMs during risk assessments. Credit Risk Analysis Use relationship graphs to understand borrower affiliations, guarantors, and exposure networks. Seeing the World in Geospatial Layers Geospatial platforms support context engineering by embedding spatial awareness into AI systems, enabling them to reason about location, proximity, movement, and environmental context. They can provide rich, structured data layers (e.g., terrain, infrastructure, demographics, weather) that can be dynamically retrieved and injected into an LLM's context window. This allows the model to generate responses that are not only linguistically coherent but also geographically grounded. For example, in disaster response, a geospatial platform can provide real-time satellite imagery, flood zones, and population density maps. This data can be translated into structured prompts or visual inputs for an AI model tasked with coordinating relief efforts or summarizing risk. Similarly, in urban planning or logistics, geospatial context helps the model understand constraints like traffic patterns, zoning laws, or accessibility. In essence, geospatial platforms act as a spatial memory layer, enriching the model's understanding of the physical world and enabling more accurate, context-aware decision-making. Financial Services Use Cases Branch Network Optimization Combine demographic, economic, and competitor data to help LLMs recommend new branch locations. Climate Risk Assessment Integrate flood zones, wildfire risk, or urban heat maps to evaluate the environmental exposure of mortgage and insurance portfolios. Context Engineering Beyond the Limits of Data, Knowledge & Truths Context engineering I believe recognizes that data is partial, and that knowledge and perhaps truth or truths needs to be situated, connected, and interpreted. Whether through graphs, time-series, vectors, tech computing platforms, or geospatial layering, AI depends on weaving the right contextual strands together. Where AI represents the loom, the five types of platforms I describe are like the spindles, needles, and dyes drawing on their respective contextual fabrics of ever changing data, driving threads of knowledge—contingent, contextual, and ready for action.
South Wales Guardian
an hour ago
- South Wales Guardian
Israel to send negotiators to Qatar for Gaza ceasefire talks
The statement also asserted that Hamas was seeking 'unacceptable' changes to the proposal. US President Donald Trump has pushed for an agreement and will host Mr Netanyahu at the White House on Monday to discuss a deal. Inside Gaza, Israeli airstrikes killed 14 Palestinians and another 10 were killed while seeking food aid, hospital officials in the embattled enclave said. And two US aid workers with the Israel-backed Gaza Humanitarian Foundation were injured in an attack at a food distribution site, which the organisation blamed on Hamas, without providing evidence. Weary Palestinians expressed cautious hope after Hamas gave a 'positive' response late Friday to the latest US proposal for a 60-day truce but said further talks were needed on implementation. 'We are tired. Enough starvation, enough closure of crossing points. We want to sleep in calm where we don't hear warplanes or drones or shelling,' said Jamalat Wadi, one of Gaza's hundreds of thousands of displaced people, speaking in Deir al-Balah. She squinted in the sun during a summer heat wave of over 30C. Hamas has sought guarantees that the initial truce would lead to a total end to the war and withdrawal of Israeli troops from Gaza. Previous negotiations have stalled over Hamas demands of guarantees that further negotiations would lead to the war's end, while Mr Netanyahu has insisted Israel would resume fighting to ensure the militant group's destruction. 'Send a delegation with a full mandate to bring a comprehensive agreement to end the war and bring everyone back. No one must be left behind,' Einav Zangauker, mother of hostage Matan Zangauker, told the weekly rally by relatives and supporters in Tel Aviv. Israeli airstrikes struck tents in the crowded Muwasi area on Gaza's Mediterranean coast, killing seven people including a Palestinian doctor and his three children, according to Nasser Hospital in the southern city of Khan Younis. Four others were killed in the town of Bani Suheila in southern Gaza. Three people were killed in three strikes in Khan Younis. Israel's army did not immediately comment. Separately, eight Palestinians were killed near a GHF aid distribution site in the southern city of Rafah, the hospital said. One Palestinian was killed near another GHF point in Rafah. It was not clear how far the Palestinians were from the sites. GHF denied the killings happened near their sites. The organisation has said no one has been shot at its sites, which are guarded by private contractors and can be accessed only by passing Israeli military positions hundreds of metres away. The army had no immediate comment but has said it fires warning shots as a crowd-control measure and only aims at people when its troops are threatened. Another Palestinian was killed waiting in crowds for aid trucks in eastern Khan Younis, officials at Nasser Hospital said. The United Nations and other international organisations have been bringing in their own supplies of aid since the war began. The incident did not appear to be connected to GHF operations. Much of Gaza's population of over two million now relies on international aid after the war has largely devastated agriculture and other food sources and left many people near famine. Crowds of Palestinians often wait for lorries and unload or loot their contents before they reach their destinations. The lorries must pass through areas under Israeli military control. Israel's military did not immediately comment. The GHF said the two American aid workers were injured on Saturday morning when assailants threw grenades at a distribution site in Khan Younis. The foundation said the injuries were not life-threatening. Israel's military said it evacuated the workers for medical treatment. The GHF, a US- and Israeli-backed initiative meant to bypass the UN, distributes aid from four sites that are surrounded by Israeli troops. Three sites are in Gaza's far south. The UN and other humanitarian groups have rejected the GHF system, saying it allows Israel to use food as a weapon, violates humanitarian principles and is not effective. Israel says Hamas has siphoned off aid delivered by the UN, a claim the UN denies. Hamas has urged Palestinians not to cooperate with the GHF. GHF, registered in Delaware, began distributing food in May to Palestinians, who say Israeli troops open fire almost every day toward crowds on roads heading to the distribution points. Several hundred people have been killed and hundreds more wounded, according to Gaza's Health Ministry and witnesses. The UN human rights office says it has recorded 613 Palestinians killed within a month in Gaza while trying to obtain aid, most of them while trying to reach GHF sites. The war began when Hamas attacked Israel on October 7, 2023, killing some 1,200 people and taking 251 others hostage. Israel responded with an offensive that has killed over 57,000 Palestinians, more than half of them women and children, according to Gaza's Health Ministry, which is led by medical professionals employed by the Hamas government. It does not differentiate between civilians and combatants, but the UN and other international organisations see its figures as the most reliable statistics on war casualties.
The Guardian
an hour ago
- The Guardian
I chaired the FCC. The 60 Minutes settlement shows Trump has weaponized the agency
It is time to unfurl the 'Mission Accomplished' banner at the Federal Communications Commission (FCC). Paramount Global, the parent of CBS Television, has agreed to pay $16m to settle a lawsuit brought by Donald Trump over the editing of a 60 Minutes interview with Kamala Harris. Presumably, the FCC can now cease its slow-walking of the Paramount-Skydance Media merger. Just two days after the president took office, the agency's new chair, Brendan Carr, inserted the FCC into the issues in the Trump lawsuit that alleged 'news distortion'. As the New York Post headlined: 'Trump's FCC pick Brendan Carr says '60 Minutes' editing scandal could affect Paramount-Skydance merger review.' That lawsuit was filed in the final week of the 2024 presidential campaign under the Texas Deceptive Trade Practices Act, a statute historically used against false advertising. The case was filed in a single-judge federal district court that one legal publication characterized as 'a favored jurisdiction for conservative legal causes and plaintiffs'. CBS characterized the case as 'without merit'. The 60 Minutes broadcast aired in October; the day before, a different excerpt had appeared on Face the Nation. Soon after, the Center for American Rights – a group that describes itself as 'a public interest law firm dedicated to protecting Americans' most fundamental constitutional rights' – filed a complaint at the FCC alleging CBS had engaged in 'significant and substantial news alteration'. The complaint was dismissed as seeking 'to weaponize the licensing authority of the FCC in a way that is fundamentally at odds with the First Amendment'. Immediately upon becoming the FCC chair, Carr reversed that decision and ordered a formal proceeding on the matter (but let stand the dismissal of a complaint against a local Fox station over its 2020 election coverage). The election of Trump and the installation of a Trump-appointed FCC chair transformed the Paramount/CBS merger from a review of the public interest merits of the transfer of broadcast licenses into a broader question that included the 60 Minutes editing. Carr told an interviewer: 'I'm pretty confident that the news distortion complaint over the 60 Minutes transcript is something that is likely to arise in the context of the FCC review of that transaction.' The formal paperwork for FCC approval of the license transfers was submitted 10 months ago, on 6 September 2024. Now that the lawsuit has been settled, it will be interesting to see how quickly the FCC acts. The CBS case is just one example of the tactical leverage the Trump FCC regularly exerts over those it regulates. Carr, who wrote the FCC chapter in the 'Project 2025' Maga blueprint, has not been shy about using this authority to achieve such political goals. Even before formally assuming the FCC chair position, Carr began exercising chair-like authority to advance the Maga agenda. This began with a letter to the CEOs of Alphabet (Google and YouTube), Meta (Facebook and Instagram), Microsoft and Apple alleging: 'you participated in a censorship cartel … [that is] an affront to Americans' constitutional freedoms and must be completely dismantled.' Going beyond traditional FCC authority, he threatened: 'As you know, Big Tech's prized liability shield, Section 230, is codified in the Communications Act, which the FCC administers.' Carr suggested he might investigate whether those editorial decisions were made in good faith. Recently, Carr conditioned the approval of Verizon's acquisition of Frontier Communications on Verizon agreeing to drop its corporate diversity, equity and inclusion (DEI) policies. Continuing his anti-diversity efforts, he launched an investigation into Comcast Corporation because it promotes DEI as 'a core value of our business'. In his pre-FCC chair days, Carr championed press freedom. In a 2021 statement, he wrote: 'A newsroom's decision about what stories to cover and how to frame them should be beyond the reach of any government official.' Once he became Trump's FCC chair, however, he not only picked up on the 60 Minutes matter, but also launched an investigation into the public broadcasters NPR and PBS 'regarding the airing of … programming across your broadcast member stations'. The FCC's regulatory authority directly covers about one-sixth of the American economy while also affecting the other five-sixths that rely on the nation's communications networks. What was once an independent, policy-based agency has been transformed into a performance-based agency, using any leverage it can discover or invent to further the Trump Maga message. Tom Wheeler was the chair of the Federal Communications Commission from 2013 to 2017
