A new report ties together two developments that point in the same direction for advanced computing: AI is moving from assisting engineers to handling core low-level work that humans may not fully inspect line by line. Citing SemiAnalysis, the story says OpenAI engineers reviewing low-level code for the company’s in-house chip effort admitted they could not fully explain how every AI-generated assembly sequence worked, even though the code had been tested and delivered strong performance. The report also says OpenAI built a low-level kernel programming language called Gluon on top of Triton, with complex hardware instructions generated by AI. At the same time, Architect Labs has released a preprint titled "Redwood: A Frontier AI Accelerator Designed, Verified, and Deployed from Scratch in 2 Weeks by AI." According to the paper, two human engineers wrote high-level functional specifications in natural language, and an AI system produced the hardware design, verification suite, and firmware stack in two weeks without using off-the-shelf commercial accelerator IP. The paper says Redwood was deployed on AMD Xilinx Versal FPGA hardware and ran an open-source large model. It also estimates that if Redwood were converted into an ASIC on an equivalent process node, its energy efficiency in edge Physical AI and low-power settings would be 3.4 times that of Nvidia Jetson. The paper further says a model running on the first-generation Redwood chip has already joined the design of the next generation.

BlockBeats reported on August 22, citing MaxForAI, that Gao Jianlin, a core figure in Tencent’s chip R&D work, has left the company and started his own venture. The new startup is focused on high-performance CPUs built on the RISC-V architecture, with an emphasis on AI servers and Agentic AI. Gao previously helped build Tencent’s FPGA hardware team, moved into AI chips in 2018, and later founded the Penglai Lab in 2020. The report says he also worked on RISC-V-related projects at Tencent and was involved in chip architecture, verification, and backend work. The company name, fundraising status and product timeline have not been disclosed.

DigiByte (DGB) employs five PoW algorithms (SHA256, Scrypt, Skein, Qubit, Odocrypt). GPU and CPU mining are unprofitable; only ASIC/FPGA work. Block reward is 581.77 DGB, TPS over 1066.

A comprehensive guide explaining Bitcoin mining pools, covering cooperative work protocols, server/client software, five reward schemes (PPS, PPLNS, etc.), and practical steps to join a pool.

This guide breaks down how Bitcoin mining works, what hardware is used, how to set up a solo mining operation, and which cost factors matter most before starting.

Vertcoin (VTC) remains one of the few cryptocurrencies minable by CPU/GPU at home, thanks to its Verthash algorithm. This guide covers its ASIC resistance, Windows mining steps, and 2023 price predictions.

Fears that quantum computers could soon crack Bitcoin keep resurfacing, but the source article argues the gap between theory and practical attack capability remains vast. In theory, the hardware required to break elliptic curve cryptography, or ECC, has fallen sharply, from about 317 million physical qubits in 2022 to fewer than 500,000 in a 2026 paper. That is a major change on paper. The problem is that current machines can only run real algorithms with about 105 usable qubits, leaving several orders of magnitude still missing. The article walks through what would actually be needed to run Shor’s algorithm against ECC, including logical qubits, error correction, decoding speed, and the production of magic states needed for non-Clifford gates such as Toffoli gates. It says the 2026 design would require around 1,200 logical qubits, about 90 million sequential Toffoli gates, roughly 500,000 physical qubits under current error-correction assumptions, and a runtime measured in minutes. It also separates abstract cryptographic risk from actual wallet exposure. For Bitcoin, addresses only become vulnerable once a public key is revealed on-chain, which is why the article highlights roughly 6.7 million BTC as already exposed. Ethereum faces a related issue because it uses the same secp256k1 curve and ECDSA signatures, while its persistent account model makes repeated address use more common.

A TechFlowPost article translated from Derrick Cui argues that practical quantum attacks on elliptic curve cryptography, or ECC, are still several orders of magnitude away even after major theoretical advances. The piece says recent research has cut the estimated hardware needed to break ECC from about 317 million physical qubits in 2022 to fewer than 500,000 in 2026, but today’s machines can run real algorithms on only about 105 qubits. It walks through what would actually be required to attack ECC with Shor’s algorithm, including logical qubits, error correction, decoding, Toffoli gates and magic-state distillation, then compares those requirements with current hardware. The article also discusses what a quantum break would mean for Bitcoin and Ethereum. In Bitcoin, the main risk is tied to addresses whose public keys have already been revealed on-chain, while Ethereum’s persistent account model would leave any wallet that has sent a transaction exposed if quantum attacks were available today. The piece concludes that the gap between theory and deployable machines remains enormous, and that no reliable timeline can be inferred from current progress alone.