Trump to sidestep Congress, pay TSA workers
<p>President Trump announced Thursday that he would sign an order to circumvent Congress and restore <a href="https://www.axios.com/2026/03/25/tsa-airport-security-quit-shutdown" target="_blank">pay for TSA workers</a>.</p><p><strong>Why it matters:</strong> The maneuver could help ease widespread delays and disruptions at U.S. airports that have threatened to raise voter ire. It also undermines efforts to strike a deal in the Senate to <a href="https://www.axios.com/2026/03/25/senate-dhs-shtudown-tsa-democrats-republicans" target="_blank">end the DHS shutdown</a>, now well into its second month.</p><hr><p><strong>Driving the news: </strong>"I am going to sign an Order instructing the Secretary of Homeland Security, <a href="https://www.axios.com/2026/03/24/mullin-confirmed-homeland-securi
Could not retrieve the full article text.
Read on Axios Tech →Axios Tech
https://www.axios.com/2026/03/26/tsa-worker-pay-white-house-senate-republicans-sidestep-congressSign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases

HarshAI: I Built a Zapier Killer in 40 Days (Open Source)
HarshAI: I Built a Zapier Killer in 40 Days (Open Source) 40 days, 90 planned features, 44% complete. Here's what I built. Why I Started Zapier is expensive. Make.com has a learning curve. I wanted something: ✅ Free open source ✅ Drag-drop builder ✅ Self-hostable ✅ Built for AI workflows So I started building HarshAI . What's Built (Days 1-40) Phase 1: Core Builder (Days 1-15) Drag-drop workflow builder Node-based interface Real-time connections Mobile-responsive design Template system Phase 2: Execution Engine (Days 16-25) Workflow execution engine Real API integrations (Gmail, Twitter, Notion, Slack) Test mode (no credentials needed) Error handling Execution history Phase 3: Advanced Features (Days 26-35) Background scheduler (cron) Email notifications Analytics dashboard Webhook trigger

40 Days of Building HarshAI: What I Learned About AI Automation
40 Days of Building HarshAI: What I Learned About AI Automation 40 days. 90 planned features. Countless lessons. Here's what building in public taught me. The Journey So Far Started March 31, 2026. Today is April 6. In 7 days, I've completed 40 days worth of MVP features. Progress: 40/90 (44.4%) 5 Big Lessons 1. Webhooks Are Harder Than They Look Day 31-35 was ALL about webhooks. What seemed simple became: HMAC signature verification (Stripe-style security) Retry logic with exponential backoff Analytics dashboard Event-based filters Lesson: Enterprise features take time. Don't underestimate. 2. Version Control for Workflows is Essential Day 39: Workflow versioning. Users WILL: Break their workflows Want to rollback Need to compare versions Built: Auto-save, version history, rollback, diff

Simple parallel estimation of the partition ratio for Gibbs distributions
arXiv:2505.18324v2 Announce Type: replace-cross Abstract: We consider the problem of estimating the partition function $Z(\beta)=\sum_x \exp(\beta(H(x))$ of a Gibbs distribution with the Hamiltonian $H:\Omega\rightarrow\{0\}\cup[1,n]$. As shown in [Harris & Kolmogorov 2024], the log-ratio $q=\ln (Z(\beta_{\max})/Z(\beta_{\min}))$ can be estimated with accuracy $\epsilon$ using $O(\frac{q \log n}{\epsilon^2})$ calls to an oracle that produces a sample from the Gibbs distribution for parameter $\beta\in[\beta_{\min},\beta_{\max}]$. That algorithm is inherently sequential, or {\em adaptive}: the queried values of $\beta$ depend on previous samples. Recently, [Liu, Yin & Zhang 2024] developed a non-adaptive version that needs $O( q (\log^2 n) (\log q + \log \log n + \epsilon^{-2}) )$ samples.

Near-Optimal Space Lower Bounds for Streaming CSPs
arXiv:2604.01400v1 Announce Type: cross Abstract: In a streaming constraint satisfaction problem (streaming CSP), a $p$-pass algorithm receives the constraints of an instance sequentially, making $p$ passes over the input in a fixed order, with the goal of approximating the maximum fraction of satisfiable constraints. We show near optimal space lower bounds for streaming CSPs, improving upon prior works. (1) Fei, Minzer and Wang (\textit{STOC 2026}) showed that for any CSP, the basic linear program defines a threshold $\alpha_{\mathrm{LP}}\in [0,1]$ such that, for any $\varepsilon > 0$, an $(\alpha_{\mathrm{LP}} - \varepsilon)$-approximation can be achieved using constant passes and polylogarithmic space, whereas achieving $(\alpha_{\mathrm{LP}} + \varepsilon)$-approximation requires $\Ome




Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!