Furious anti-ICE protesters threw snowballs at feds to protect Chicago Tren de Argua gangbanger, DHS says

nypostPublicado em 2025-12-08Última atualização em 2025-12-08

Resumo

The Department of Homeland Security stated that an illegal immigrant from Venezuela, Luis Jesus Acosta Gutierrez, was arrested by ICE after a violent confrontation in Elgin, Illinois. Gutierrez, a suspected Tren de Aragua gang member, allegedly rammed an ICE vehicle during a traffic stop, fled on foot, and barricaded himself in an apartment. While agents negotiated his surrender, a large crowd of anti-ICE protesters gathered and assaulted federal agents with rocks, bottles, and snowballs. The local police declined to assist, citing the Illinois Trust Act. Gutierrez had previously been released by the Biden administration with Temporary Protected Status after entering the U.S. illegally in 2023, but his status was terminated last month. He now faces deportation proceedings.

The illegal immigrant who Chicago anti-ICE protester tried to protect by hurling rocks and snowballs at the feds is an alleged Tren de Aragua gangbanger who had rammed officer as they tried to arrest him, the Department of Homeland Security said Monday.

ICE arrested Luis Jesus Acosta Gutierrez — who entered the US under former President Joe Biden — following the impromptu riot on a snowy residential street in Elgin, Illinois, in which dozens of protesters lobbed rocks, bottles and icy snowballs at federal agents who had been chasing Gutierrez, according to DHS.

3
Luis Jesus Acosta Gutierrez, a suspected Tren de Aragua gang member, was arrested by ICE after the melee in the streets of suburban Chicago on Saturday. DHS

Immigration agents had attempted to conduct a traffic stop on Gutierrez, when he smashed his car into one of the agency’s vehicles, sending it careening into a tree.

He then fled the crash on foot before barricading himself in a stranger’s apartment.

The illegal immigrant from Venezuela then went out on the balcony and spoke to ICE agents, who tried to get him to exit the apartment and surrender.

Meanwhile, a large crowd of anti-ICE protesters amassed and began assailing the agents with snowballs and other projectiles, but DHS said local police “refused” to assist in protecting them.

3
Dozens of anti-ICE protesters descended on the federal agents as they tried to arrest Gutierrez, pelting them with rocks, bottles and snowballs. X/@KimKatieUSA

The Elgin Police Department confirmed as much in a statement on Facebook in the immediate aftermath of the unruly skirmish.

“The Elgin Police Department will continue to respond to any calls for service and determine the appropriate action within the parameters of the Illinois Trust Act which prohibits Elgin officers from assisting with federal immigration enforcement operations.”

Gutierrez was taken into custody after several hours of negotiating, and it wasn’t his first run-in with immigration authorities.

3
The Department of Homeland Security said Gutierrez intentionally rammed his car into an ICE vehicle, sending it careening into a tree. DHS

He was arrested at the border after entering the country illegally in April, 2023, but released by the Biden administration — which took the added step of granting him Temporary Protected Status (TPS).

“This suspected TdA gang member was not only released into our country by the Biden administration but also granted Temporary Protected Status,” DHS Assistant Secretary Tricia McLaughlin said in a statement.

“This yet again underscores the serious lack of vetting by Biden administration on the millions of aliens they allowed to come into the country.”

Gutierrez’s TPS was terminated last month, and DHS says he’s now facing deportation proceedings.

Leituras Relacionadas

Understanding the Q2 Crypto Market in 5 Charts: RWA Explosion, Fundamentals Continue to Recover

Summary of Q2 Crypto Market: RWA Boom and Continued Fundamental Recovery The second quarter of 2026 presented a mixed picture for the crypto market. While major crypto asset prices declined by 36% in H1 2026, the fundamentals of the industry showed significant strength. Key highlights from Bitwise's market review include: 1. **Divergence Between Crypto Stocks and Tokens:** Crypto-related public equities, tracked by the Bitwise Crypto Innovators 30 Index, rose 23% in H1, outperforming most major asset classes. This signals robust investment opportunities within the crypto ecosystem, such as Bitcoin miners benefiting from AI and traditional finance firms deepening crypto integration, even during a bear market for tokens. 2. **Substantial Crypto Application Revenue:** Leading decentralized applications generated a combined $5.9 billion in revenue over the past 12 months, with top protocols like PancakeSwap, Hyperliquid, and Aave each nearing $1 billion. This demonstrates the existence of real, revenue-generating businesses within the sector. 3. **Breakout Growth in Real-World Asset (RWA) Tokenization:** The total value of tokenized real-world assets reached a record $33 billion in Q2, up 12% quarterly and 45% year-to-date. Growth is driven by tokenized U.S. Treasuries, corporate credit, equities, and venture capital shares, indicating accelerating institutional adoption. 4. **Expanding Prediction Markets:** Prediction market open interest hit a new high of $1.8 billion in Q2, with sports being a key category. Quarterly trading volume also reached a record $43 billion. Platforms like Polymarket represent a form of mainstream, albeit often unaware, adoption of crypto infrastructure for event betting, with further growth expected around the U.S. midterm elections. 5. **Attractive Profile of Crypto Equities:** The Bitwise Crypto Innovators 30 Index exhibited low 90-day rolling correlations with most major assets (developed market stocks, EM stocks, REITs, bonds, gold) and negative correlation with commodities. This combination of high returns and portfolio diversification is highly attractive to institutional investors. In conclusion, despite weak token prices, core industry fundamentals—including user activity, business revenues, and institutional adoption—continue to advance, building a strong foundation for the next market cycle.

Foresight NewsHá 25m

Understanding the Q2 Crypto Market in 5 Charts: RWA Explosion, Fundamentals Continue to Recover

Foresight NewsHá 25m

GPT-5.6 Cracks a 50-Year-Old Math Problem in 1 Hour, 64 AIs Claim the Crown Jewel of Graph Theory

OpenAI announced that its AI model, GPT-5.6 Sol Ultra, has successfully proved the 50-year-old Cycle Double Cover (CDC) conjecture in graph theory in under an hour. This long-standing problem, posed independently by several prominent mathematicians, states that every bridgeless finite undirected graph contains a set of cycles where each edge is covered exactly twice. The breakthrough was achieved using a novel "parallel test-time computation" (TTC) approach. Instead of a single AI working sequentially, the system deployed 64 concurrent AI agents, each exploring distinct proof strategies—from algebraic perspectives to structural induction. The process included strict protocols to avoid common research pitfalls: initial exploration of fundamentally different paths, preventing herd mentality by not revealing the most promising direction, and employing a "critic squad" of agents to rigorously attack and verify every proposed proof step. The system forbade vague assertions, demanding concrete lemmas and constructions. The resulting proof, generated by GPT-5.6 and formatted with Codex, employed a sophisticated multi-step strategy. It first reduced the general case to cubic graphs, then leveraged Tutte's group-flow theorem to establish the existence of a nowhere-zero 8-flow on the graph. A key inventive step was introducing a "two-element set" labeling scheme (Lemma 2.1), which, if satisfied, guarantees a cycle double cover. The AI then transformed this combinatorial condition into a large system of linear equations (Lemma 2.2), using linear algebra over finite fields to conclusively demonstrate that a solution always exists. Researchers highlighted that parallel TTC dramatically compressed the reasoning time, making deep, extended AI problem-solving practically feasible. While some observers marveled at the implications for mathematics and science, others questioned whether parallel breadth can fully substitute for deep, continuous logical chains. Nonetheless, this achievement marks a significant advance in AI's autonomous capacity for high-level abstract reasoning and complex proof generation.

marsbitHá 30m

GPT-5.6 Cracks a 50-Year-Old Math Problem in 1 Hour, 64 AIs Claim the Crown Jewel of Graph Theory

marsbitHá 30m

Prompt Engineering Paper Accepted at ICML 2026 Sparks Heated Debate Among Netizens

A paper on prompt engineering, titled "Verbalized Sampling (VS)," has been accepted by the prestigious machine learning conference ICML 2026, sparking significant debate online. The paper addresses the problem of "mode collapse" in large language models (LLMs), where models tend to produce repetitive, safe, and homogeneous outputs. Instead of proposing new training algorithms or model architectures, the authors introduce a simple yet effective prompt-based method. The core technique, Verbalized Sampling, instructs the model to generate multiple responses (e.g., five jokes) while also outputting a possible probability value for each. This prompt adjustment alone was shown to significantly increase output diversity by 1.6x to 2.1x in creative writing tasks, without compromising factual accuracy or safety. The authors argue that the root cause of mode collapse lies not in optimization algorithms but in the "typicality bias" present in human preference data used for alignment. Human annotators naturally favor familiar and fluent text, which steers models toward conservative outputs. The VS method aims to counteract this by leveraging the model's inherent pre-training distribution during inference. The paper's acceptance has led to polarized reactions. Critics argue that prompt engineering lacks the theoretical depth and algorithmic innovation expected from top-tier conferences like ICML, questioning its novelty, generalizability across models, and experimental scale. Some draw parallels to reproducibility crises in other fields, citing a potential over-reliance on empirical results. Supporters, including an author who responded online, defend the work's rigor. They emphasize its comprehensive problem analysis, theoretical grounding, mathematical derivation, and extensive quantitative experiments. Proponents compare VS to seminal techniques like Chain-of-Thought (CoT) prompting, suggesting that inference-stage methods are becoming a core part of ML research capable of expanding model capabilities without retraining. The research was conducted by a team from Northeastern University, Stanford University, and West Virginia University, with Jiayi Zhang, Simon Yu, and Derek Chong as co-first authors.

marsbitHá 32m

Prompt Engineering Paper Accepted at ICML 2026 Sparks Heated Debate Among Netizens

marsbitHá 32m

Trading

Spot
活动图片