Fed slashes interest rates, but issues mixed forward guidance

cointelegraphPubblicato 2025-12-10Pubblicato ultima volta 2025-12-10

Introduzione

The Federal Reserve cut interest rates by 25 basis points to a range of 3.5%–3.75%. Chair Jerome Powell delivered mixed guidance, noting upside risks to inflation and downside risks to employment. Analysts suggest this may curb a Bitcoin rally, with only one rate cut expected in 2026. Market expectations show low probability of a cut in January 2026. Powell’s term ends in May 2026, and President Trump is considering replacements, with Kevin Hassett as a potential candidate. Trump has urged the next Fed chair to lower rates further.

The Federal Reserve slashed interest rates by 25 basis points on Wednesday to a target range of 3.5% to 3.75%. However, mixed comments from Federal Reserve Chair Jerome Powell will likely quell a Bitcoin price rally until the rate-cutting cycle resumes in 2026, analysts say.

“In the near term, risks to inflation are tilted to the upside and risks to employment to the downside, a challenging situation. There is no risk-free path for policy,” Powell said at Wednesday’s Federal Reserve Open Committee (FOMC) meeting.

These comments were not as “hawkish” as some analysts expected, but the Federal Reserve is now expected to issue only one rate cut in 2026 under Powell’s leadership, according to market analyst and Coinbureau founder Nic Puckrin. He added:

“Attention will turn to liquidity and the Fed’s balance sheet policy in early 2026. However, despite the Treasury bill purchase announced today, quantitative easing isn’t coming until things start breaking, and that always means more volatility and potential pain.”

Low Interest rates fuel risk-on assets, such as Bitcoin (BTC), but only 24.4% of traders expect a rate cut at the next FOMC meeting in January 2026, according to data from the CME Group.

Interest rate target probabilities for January 2026. Source: CME

US President Donald Trump has been weighing Powell’s replacement, with National Economic Council director Kevin Hassett widely reported as the frontrunner for the position. Hassett is also a former adviser on Coinbase’s Academic and Regulatory Advisory Council

Related: Short the dip and buy the rip? What FOMC outcomes reveal about Bitcoin price action

Powell gives mixed remarks, but Trump says the next Fed chair will slash rates

Powell said consumer spending and business investment remain “solid” and added that layoffs and hiring remain low. However, inflation remains “somewhat elevated” above the Federal Reserve’s 2% inflation target, while the housing sector is considered still “weak.”

The Fed reached these conclusions using available market data, but Powell acknowledged that it is missing months of public economic reports due to the US government shutdown.

Jerome Powell delivers remarks following the December 2025 FOMC meeting. Source: Federal Reserve

Trump has already pressured the next Fed chair to slash rates. Powell’s term is set to expire in May 2026.

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Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

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Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

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Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

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Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

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