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DAIR.AI
RT @omarsar0: MiniMax just dropped M2.5, a top-tier open-weight model.
Already competitive with models like Opus 4.6.
The speed at which open-weight models are improving is wild.
It's fast and surprisingly fluent at generating and operating Word, Excel, and PowerPoint files.
But the bigger deal for me is using M2.5 for long-horizon agents.
@MiniMax_AI's M2.5 is one of the first open models I've seen that show serious signs of improvement on long-running tasks.
M2.5 was trained with RL across hundreds of thousands of complex real-world environments.
The model learned to optimize its actions through planning, which is a meaningful difference from models that are merely prompted to plan.
When your agent runs for hours across multi-step tasks, a model that plans natively will drift less and waste fewer tokens.
You can try it on coding tasks, but I think the bigger unlock is using it as the backbone for an agent that operates across your full workspace (code, docs, spreadsheets, browser)
Benchmark results are nice too:
80.2% on SWE-Bench Verified. 76.3% on BrowseComp. 76.8% on BFCL for agentic tool-calling. 51.3% on Multi-SWE-Bench.
All of these map directly to what long-running agents need, which are coding, searching, tool use, and multi-step execution.
And here is what makes it practical:
$1 per hour at 100 tps!
With only 10B activated parameters, it's the smallest Tier-1 model, which makes self-hosting real. 37% faster execution times on complex tasks.
Find all the resources below:
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RT @omarsar0: MiniMax just dropped M2.5, a top-tier open-weight model.
Already competitive with models like Opus 4.6.
The speed at which open-weight models are improving is wild.
It's fast and surprisingly fluent at generating and operating Word, Excel, and PowerPoint files.
But the bigger deal for me is using M2.5 for long-horizon agents.
@MiniMax_AI's M2.5 is one of the first open models I've seen that show serious signs of improvement on long-running tasks.
M2.5 was trained with RL across hundreds of thousands of complex real-world environments.
The model learned to optimize its actions through planning, which is a meaningful difference from models that are merely prompted to plan.
When your agent runs for hours across multi-step tasks, a model that plans natively will drift less and waste fewer tokens.
You can try it on coding tasks, but I think the bigger unlock is using it as the backbone for an agent that operates across your full workspace (code, docs, spreadsheets, browser)
Benchmark results are nice too:
80.2% on SWE-Bench Verified. 76.3% on BrowseComp. 76.8% on BFCL for agentic tool-calling. 51.3% on Multi-SWE-Bench.
All of these map directly to what long-running agents need, which are coding, searching, tool use, and multi-step execution.
And here is what makes it practical:
$1 per hour at 100 tps!
With only 10B activated parameters, it's the smallest Tier-1 model, which makes self-hosting real. 37% faster execution times on complex tasks.
Find all the resources below:
tweet
Dimitry Nakhla | Babylon Capitalยฎ
15 Quality Stocks With Forward FCF Yields Above the Risk-Free Rate (3-Month T-Bill: 3.60%) ๐ต
1. $FICO 3.61%
2. $NFLX 3.61%
3. $TDG 3.65%
4. $MA 3.78%
5. $MCO 3.84%
6. $MSCI 4.03%
7. $CPRT 4.07%
8. $NVO 4.09%
9. $V 4.12%
10. $APP 4.32%
11. $MANH 4.53%
12. $SPGI 5.04%
13. $NOW 5.30%
14. $INTU 6.22%
15. $CSU 7.10%
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15 Quality Stocks With Forward FCF Yields Above the Risk-Free Rate (3-Month T-Bill: 3.60%) ๐ต
1. $FICO 3.61%
2. $NFLX 3.61%
3. $TDG 3.65%
4. $MA 3.78%
5. $MCO 3.84%
6. $MSCI 4.03%
7. $CPRT 4.07%
8. $NVO 4.09%
9. $V 4.12%
10. $APP 4.32%
11. $MANH 4.53%
12. $SPGI 5.04%
13. $NOW 5.30%
14. $INTU 6.22%
15. $CSU 7.10%
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Brady Long
RT @thisguyknowsai: I reverse-engineered the actual prompting frameworks that top AI labs use internally.
Not the fluff you see on Twitter.
The real shit that turns vague inputs into precise, structured outputs.
Spent 3 weeks reading OpenAI's model cards, Anthropic's constitutional AI papers, and leaked internal prompt libraries.
Here's what actually moves the needle:
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RT @thisguyknowsai: I reverse-engineered the actual prompting frameworks that top AI labs use internally.
Not the fluff you see on Twitter.
The real shit that turns vague inputs into precise, structured outputs.
Spent 3 weeks reading OpenAI's model cards, Anthropic's constitutional AI papers, and leaked internal prompt libraries.
Here's what actually moves the needle:
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AkhenOsiris
RT @JaredSleeper: Amidst all of this, January was the biggest month for SaaS hiring in the last two years...๐คฆโโ๏ธ https://t.co/XG3hh6aWkK
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RT @JaredSleeper: Amidst all of this, January was the biggest month for SaaS hiring in the last two years...๐คฆโโ๏ธ https://t.co/XG3hh6aWkK
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Javier Blas
RT @carbellorin: These companies didnโt just get access. All of them have existing contracts/projects in Venezuela. This is why GL50 was issued.
The GL49 was issued too, authorising companies to negotiate with the government/PDVSA and to enter into contingent contracts for โnewโ oil and gas investments.
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RT @carbellorin: These companies didnโt just get access. All of them have existing contracts/projects in Venezuela. This is why GL50 was issued.
The GL49 was issued too, authorising companies to negotiate with the government/PDVSA and to enter into contingent contracts for โnewโ oil and gas investments.
Can't help but notice who ISN'T on this list of companies who just gained access to invest and operate in Venezuelan upstream. https://t.co/qrLcy4bd48 tweet
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Benjamin Hernandez๐
$RIVN: +27% | Q4 Earnings & R2 Outlook
Rivian losses narrowed significantly ($0.54 vs $0.68). 2026 delivery guidance of 67k units is solid. Institutional capital is rotating from Tesla into Rivian as the growth alternative. $17.50 is the key.
$SOC $ASST $OPEN $RADX $PULM https://t.co/qAHyAvsm9G
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$RIVN: +27% | Q4 Earnings & R2 Outlook
Rivian losses narrowed significantly ($0.54 vs $0.68). 2026 delivery guidance of 67k units is solid. Institutional capital is rotating from Tesla into Rivian as the growth alternative. $17.50 is the key.
$SOC $ASST $OPEN $RADX $PULM https://t.co/qAHyAvsm9G
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Dimitry Nakhla | Babylon Capitalยฎ
โThe biggest beneficiaries, however, would be real miners with real mines.โ
One of the hardest lines in the Akre Shareholder Letter ๐
$NOW $CRM $CSU $ADBE $SNPS $CDNS $ROP $SAP $INTU $ADSK https://t.co/eDWzIgNmSG
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โThe biggest beneficiaries, however, would be real miners with real mines.โ
One of the hardest lines in the Akre Shareholder Letter ๐
$NOW $CRM $CSU $ADBE $SNPS $CDNS $ROP $SAP $INTU $ADSK https://t.co/eDWzIgNmSG
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Dimitry Nakhla | Babylon Capitalยฎ
Seth Klarman โ Baupost Group Q4 25โ 13F
Top 5 holdings: $QSR $AMZN $WTW $ELV $UNP
Top Buys: $AMZN $TRU $CCS $LYV
Top Sales: $GOOG $DG
___
Who wouldโve thought โ the author of ๐๐ข๐ณ๐จ๐ช๐ฏ ๐ฐ๐ง ๐๐ข๐ง๐ฆ๐ต๐บ now has $AMZN as his second-largest holding.
Source: Dataroma https://t.co/xcsVO1gxwo
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Seth Klarman โ Baupost Group Q4 25โ 13F
Top 5 holdings: $QSR $AMZN $WTW $ELV $UNP
Top Buys: $AMZN $TRU $CCS $LYV
Top Sales: $GOOG $DG
___
Who wouldโve thought โ the author of ๐๐ข๐ณ๐จ๐ช๐ฏ ๐ฐ๐ง ๐๐ข๐ง๐ฆ๐ต๐บ now has $AMZN as his second-largest holding.
Source: Dataroma https://t.co/xcsVO1gxwo
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Dimitry Nakhla | Babylon Capitalยฎ
RT @DimitryNakhla: โWhat is dead may never die.โ $SPGI https://t.co/K4UWFX0PwW
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RT @DimitryNakhla: โWhat is dead may never die.โ $SPGI https://t.co/K4UWFX0PwW
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