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	<title>Tech &#8211; Firethering</title>
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	<link>https://firethering.com</link>
	<description>Firethering is Your Hub for AI, Open Source and Tech That Actually Matters</description>
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	<title>Tech &#8211; Firethering</title>
	<link>https://firethering.com</link>
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	<item>
		<title>OpenAI Built Its First AI Chip. It&#8217;s Not Trying to Replace NVIDIA.</title>
		<link>https://firethering.com/openai-first-ai-chip/</link>
					<comments>https://firethering.com/openai-first-ai-chip/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Thu, 25 Jun 2026 07:16:47 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[Trends]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[OpenAI]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7460</guid>

					<description><![CDATA[When the news broke that OpenAI had built a custom chip, the instinct was to frame it as a NVIDIA story. Another lab trying to cut the cord, reduce dependence on H100s, claw back some margin from the company that's been printing money off the AI boom.

That's not quite what's happening here.

The chip is called Jalapeño, built with Broadcom, and it doesn't touch training at all. It's an inference chip, meaning it only runs models after they're already built, when a user sends a message and ChatGPT has to respond. The compute-heavy work of actually training those models still runs on NVIDIA hardware. OpenAI isn't replacing NVIDIA. It's going after a different part of the problem entirely, the part that happens millions of times a day, every time someone uses one of their products.

That distinction matters because inference is where AI costs actually accumulate at scale. Training happens once per model. Inference never stops.]]></description>
		
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			</item>
		<item>
		<title>GLM-5.2 Is the Closest an Open Model Has Come to Claude</title>
		<link>https://firethering.com/glm-5-2-is-the-closest-an-open-model-has-come-to-claude/</link>
					<comments>https://firethering.com/glm-5-2-is-the-closest-an-open-model-has-come-to-claude/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Thu, 18 Jun 2026 12:46:57 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[AI Models]]></category>
		<category><![CDATA[AI]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7414</guid>

					<description><![CDATA[What does it take for an open-weight model to stop chasing Claude and actually beat it?

Every open-weight release for two years has told some version of the same story: closer, but not quite. The chart shrinks, the wording softens to "competitive with," and the conversation moves on until the next model repeats the cycle.

GLM-5.2 breaks that pattern. The model is built to survive long, messy coding work, the kind that runs for hours without losing the thread. That's the pitch its maker is leading with. But scroll down their own benchmark table and something else is sitting there quietly: on a couple of standard math evals, this open model isn't approaching Claude Opus 4.8, GPT-5.5, or Gemini 3.1 Pro. It's beating all three, on the same table.

It loses plenty of ground elsewhere, and that part matters just as much as the wins. But a model anyone can download under an MIT license, with no usage restrictions attached, coming out ahead of the lab everyone else measures themselves against, is worth pausing on before getting to what the rest of the numbers actually say.]]></description>
		
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			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>5 Open-Source AI Tools You Probably Haven&#8217;t Tried Yet</title>
		<link>https://firethering.com/open-source-ai-tools-you-probably-havent-tried-yet/</link>
					<comments>https://firethering.com/open-source-ai-tools-you-probably-havent-tried-yet/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Tue, 16 Jun 2026 19:28:06 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[AI Picks]]></category>
		<category><![CDATA[Picks]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[AI Tools]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7391</guid>

					<description><![CDATA[Every week brings another open source AI release, and most of them require setting up a Python environment. Find out the model card lied about VRAM requirements. By the time something actually runs, the appeal has mostly worn off.

The five tools below skip most of that. One turns image and video generation into something closer to a desktop app. One gives DeepSeek an actual workspace instead of a browser tab. One builds UI prototypes using coding agents you probably already have installed. One quietly builds a memory system out of your own apps. And one is, literally, a desktop pet.]]></description>
		
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			</item>
		<item>
		<title>Claude Mythos 5 Was Too Powerful to Ship. Anthropic Released Fable 5 Instead.</title>
		<link>https://firethering.com/claude-mythos-5-fable-5-anthropic-release/</link>
					<comments>https://firethering.com/claude-mythos-5-fable-5-anthropic-release/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Tue, 09 Jun 2026 21:44:46 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[AI Models]]></category>
		<category><![CDATA[Trends]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Anthropic]]></category>
		<category><![CDATA[Claude]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7349</guid>

					<description><![CDATA[Anthropic gave stripe early access to Fable 5 and set it loose on a 50 million line Ruby codebase. The migration that would have taken a full engineering team over two months got done in a day.

That's a real company's real codebase and a task with real consequences if it goes wrong. Anthropic leads with it because it's the kind of result that's hard to argue with &#038; because it sets up everything else they need to tell you about why this launch looks the way it does.

Because here's the thing. The model Anthropic actually built Claude Mythos 5, isn't what most people are getting today. What's going live for general use is Claude Fable 5. Same underlying model. Different version. The parts Anthropic decided were too dangerous for public release got a separate wrapper, a separate name, and a separate approval process controlled in part by the US government.]]></description>
		
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			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Amazon Just Made Print-on-Demand a Default Shopping Feature. The Platforms Built Around It Should Be Worried.</title>
		<link>https://firethering.com/amazon-added-ai-merch-to-its-shopping-app/</link>
					<comments>https://firethering.com/amazon-added-ai-merch-to-its-shopping-app/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Mon, 08 Jun 2026 17:30:14 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[Trends]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[amazon]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7298</guid>

					<description><![CDATA[Amazon didn't hold a press event for this.  Just a quiet update to the Shopping app, tap the Alexa icon, describe what you want on a T-shirt, watch it appear. Add to cart. Prime shipping handles the rest.

That's it. That's the whole barrier now.

For years, turning an idea into a physical product meant either learning design tools, hiring someone who had, or finding a platform that made it slightly less painful. Print-on-demand services like Redbubble and Fourthwall built real businesses around that problem. 

Amazon just solved that problem too.]]></description>
		
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			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Ideogram 4 Topped the Open-Weight Leaderboard. Then We Read the License.</title>
		<link>https://firethering.com/ideogram-4-open-weight/</link>
					<comments>https://firethering.com/ideogram-4-open-weight/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Sat, 06 Jun 2026 14:31:24 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[AI Models]]></category>
		<category><![CDATA[Trends]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[AI Image Model]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7281</guid>

					<description><![CDATA[Ideogram was founded by former Google Brain researchers who worked on Imagen, Google's own text-to-image system. When that team releases an open-weight model, you pay attention.

Ideogram 4 tops the open-weight design leaderboard by a margin that isn't close. Professional designers picked it first in blind typography tests nearly half the time. At 9.3B parameters it beats open models three times its size on text rendering.

Then we read the license.]]></description>
		
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			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Google Built Gemma 4 12B Without Multimodal Encoders</title>
		<link>https://firethering.com/google-built-gemma-4-12b-without-multimodal-encoders/</link>
					<comments>https://firethering.com/google-built-gemma-4-12b-without-multimodal-encoders/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Thu, 04 Jun 2026 12:01:22 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[AI Models]]></category>
		<category><![CDATA[Trends]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Google]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7257</guid>

					<description><![CDATA[Every multimodal model you've used has the same basic system. Text goes in one way, images go through a vision encoder first, audio goes through an audio encoder first, and then everything gets handed off to the language model in a form it can work with. The encoders are load-bearing and you don't just remove them.Google actually removed them.Gemma 4 12B takes raw image patches and raw audio waveforms and projects them directly into the same embedding space as text tokens. There is no vision encoder or audio encoder. One decoder handling everything.]]></description>
		
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			</item>
		<item>
		<title>MiniMax M3 Shows What Happens When AI Stops Thinking in Turns</title>
		<link>https://firethering.com/minimax-m3-open-weight-model/</link>
					<comments>https://firethering.com/minimax-m3-open-weight-model/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Tue, 02 Jun 2026 19:25:39 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[AI Models]]></category>
		<category><![CDATA[Trends]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[AI Agents]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7236</guid>

					<description><![CDATA[Most models quit around submission 30 because they stop finding improvement and exit on their own. That's what happened when MiniMax ran a CUDA kernel optimization task against a field of frontier models. Every model except two called it done within the first 30 submissions.

M3's best result came on submission 145. After 24 hours. After multiple plateaus where the numbers stopped moving and a reasonable model would have concluded there was nothing left to find.

That's the thing MiniMax released yesterday. An AI model with a 1M token context window, native multimodality, and apparently a problem with knowing when to stop.]]></description>
		
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			</item>
		<item>
		<title>Anthropic Files for an IPO. AI Is Entering Its Public Company Era.</title>
		<link>https://firethering.com/anthropic-ipo-ai-public-company-era/</link>
					<comments>https://firethering.com/anthropic-ipo-ai-public-company-era/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Mon, 01 Jun 2026 19:05:12 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[Trends]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Anthropic]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7232</guid>

					<description><![CDATA[Anthropic has officially taken its first step toward becoming a public company.

In a brief announcement on Monday, the company said it had confidentially submitted a draft S-1 registration statement to the U.S. Securities and Exchange Commission for a proposed initial public offering. The filing doesn't reveal a share price, a fundraising target, or even a timeline. For now, it simply gives Anthropic the option to go public once the SEC review process is complete.

Just a few years ago, Anthropic was a small group of former OpenAI researchers trying to build an alternative vision for advanced AI. Today, it sits among the handful of companies shaping the industry's future and that's why this filing matters.

It's one of the world's most influential AI labs beginning the transition from a privately funded research company to a business that may eventually answer to public shareholders.

For most of the AI boom, the biggest bets were made behind closed doors. Venture firms, sovereign wealth funds, and tech giants supplied the capital while the public watched from the outside. Anthropic's filing suggests that era may be starting to change.]]></description>
		
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			</item>
		<item>
		<title>OpenAI Says Its AI Solved an 80-Year-Old Math Problem. The Proof Surprised Mathematicians.</title>
		<link>https://firethering.com/openai-ai-solves-80-year-old-math-problem/</link>
					<comments>https://firethering.com/openai-ai-solves-80-year-old-math-problem/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Mon, 01 Jun 2026 16:53:38 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[Trends]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[OpenAI]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=7219</guid>

					<description><![CDATA[OpenAI says one of its internal reasoning models has solved a math problem that has been there on mathematicians' desks since 1946.

The problem, first posed by legendary mathematician Paul Erdős, looks almost absurdly simple. Given a set of points on a flat plane, how many pairs can be exactly one unit apart? People have spent nearly 80 years trying to pin down the answer.

OpenAI's model didn't just make progress on the problem. According to the company, it disproved a longstanding conjecture that many researchers believed was essentially correct.]]></description>
		
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